{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interpolation with `Interpolations.jl`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a brief exposé of the rewrite of Tim Holy's `Grid.jl` that started as a metaprogramming experiment, and is now becoming good enough to start working seriously toward feature parity. `Interpolations.jl` implements [(cardinal) B-splines](http://en.wikipedia.org/wiki/B-spline#Cardinal_B-spline), i.e. interpolating polynomial functions which are fit to the data and to each other."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll start with a simple 1D example - interpolating $f(x) = sin(x)$ over one period, using 10 grid points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx = 10\n",
    "f(x) = sin(2pi/(nx-1) * (x-1))\n",
    "\n",
    "xcoarse = 1:nx\n",
    "ycoarse = f.(xcoarse);\n",
    "\n",
    "xfine = 1:0.1:xcoarse[end]\n",
    "yfine = f.(xfine);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An interpolation object is basically an array that supports indexing with any real nubmers. Currently, only a few different variants are implemented, but my hope is to extend this close to feature parity with `Grid.jl` quite soon.\n",
    "\n",
    "To create an interpolation object, simply call the constructor `Interpolation`, providing the array and some configuration for the interpolation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10-element interpolate(::Array{Float64,1}, BSpline(Linear()), OnGrid()) with element type Float64:\n",
       "  0.0        \n",
       "  0.642788   \n",
       "  0.984808   \n",
       "  0.866025   \n",
       "  0.34202    \n",
       " -0.34202    \n",
       " -0.866025   \n",
       " -0.984808   \n",
       " -0.642788   \n",
       " -2.44929e-16"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using Interpolations\n",
    "\n",
    "yitp = interpolate(ycoarse, BSpline(Linear()), OnGrid())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a couple of noteworthy points here. First, note the two extra arguments to `interpolate`: `BSpline(Linear)` and `OnGrid`. These options determine the behavior of the interpolating function inside the domain. `Linear` tells us that it will be a linear interpolation (duh...) and `OnGrid` tells us that the data points are located at *cell boundaries*. In other words, for `yitp[x]` interpolation will be performed like this:\n",
    "\n",
    "    begin\n",
    "        ix = ifloor(x)\n",
    "        dx = x - ix\n",
    "        (1-dx)*ycoarse[ix] + dx*ycoarse[ix+1]\n",
    "    end\n",
    "\n",
    "A B-spline interpolation is basically a piecewise polynomial function, where each grid cell is associated with its own polynomial. The coefficients are chosen such that the interpolating function passes through all the data points, while the transitions between polynomials are as smooth as possible given their degree. For example, each grid cell in a linear interpolation (such as `yitp`) is associated with a straight line, and the interpolation is continuous (with discontinuous derivative) over the entire domain.\n",
    "\n",
    "`OnGrid`, as used when creating `yitp`, signifies that the data points are located on *cell boundaries*; in other words, the interpolating polynomial will be the same for the entire interval between two data points (and, in fact, will be exactly the straight line connecting the dots). Other interpolation types in `Interpolations.jl` are `OnCell`, signifying that the data points are at the *center*  of each grid cell. In such an interpolation, an interval such as $(2.5,3.5)$ will be represented by a single polynomial, rather than transitioning at $x=3$.\n",
    "\n",
    "Let's take a look at how the different interpolation degrees behave:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "yitp_const = interpolate(ycoarse, BSpline(Constant()), OnCell())\n",
    "yconst = [yitp_const[x] for x in xfine]\n",
    "\n",
    "yitp_linear = interpolate(ycoarse, BSpline(Linear()), OnGrid())\n",
    "ylinear = [yitp_linear[x] for x in xfine]\n",
    "\n",
    "yitp_quadratic = interpolate(ycoarse, BSpline(Quadratic(Line())), OnCell())\n",
    "yquadratic = [yitp_quadratic[x] for x in xfine];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll notice that when creating the quadratic interpolation, we had to give another input parameter to the interpolation type: a `ExtendInner` instance.\n",
    "\n",
    "All interpolations of quadratic degree or higher require a prefiltering step, which entails solving a tridiagonal system of equations (details can be found for example in [this paper](http://dx.doi.org/10.1109/42.875199)), in order to make the interpolating function pass through the data points. `Interpolations.jl` takes care of solving this system for you, but in order to close the system a boundary condition is requred. `ExtendInner` simply means that the outermost well-defined polynomial will be extended all the way to the end. Quadratic interpolation is `OnCell`, so each piece is centered around a datapoint, and the polynomial coefficients are dependent on three data points; thus, the outermost well-defined polynomials are the ones centered on $x=2$ and $x=9$ (i.e. with domains $[1.5,2.5]$ and $[8.5,9.5]$, respectively). With `ExtendInner`, these polynomials are used all the way to the edge, i.e. on the domains $[1,2.5]$ and $[8.5,10]$ respectively. Other boundary conditions (yet to be implemented) are `Flat` (with $f' = 0$ at the edges), `Line` (with $f'' = 0$ at the edges), as well as `Reflect` and `Periodic` (which will both need to be combined with corresponding extrapolation behaviors, also yet to be implemented, in order to make sense...)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's see the implemented functionality in action!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
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       "     xmlns:gadfly=\"http://www.gadflyjl.org/ns\"\n",
       "     version=\"1.2\"\n",
       "     width=\"141.42mm\" height=\"100mm\" viewBox=\"0 0 141.42 100\"\n",
       "     stroke=\"none\"\n",
       "     fill=\"#000000\"\n",
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       "f:e,unbind:b(this.node||document,d,e,f||this)}));return this};a[\"un\"+d]=A[\"un\"+d]=function(a){for(var b=this.events||[],e=b.length;e--;)if(b[e].name==d&&(b[e].f==a||!a)){b[e].unbind();b.splice(e,1);!b.length&&delete this.events;break}return this}})(v[y]);A.hover=function(a,b,d,e){return this.mouseover(a,d).mouseout(b,e||d)};A.unhover=function(a,b){return this.unmouseover(a).unmouseout(b)};var r=[];A.drag=function(b,d,f,h,n,p){function u(r,v,w){(r.originalEvent||r).preventDefault();this._drag.x=v;\n",
       "this._drag.y=w;this._drag.id=r.identifier;!q.length&&a.mousemove(e).mouseup(l);q.push({el:this,move_scope:h,start_scope:n,end_scope:p});d&&k.on(\"snap.drag.start.\"+this.id,d);b&&k.on(\"snap.drag.move.\"+this.id,b);f&&k.on(\"snap.drag.end.\"+this.id,f);k(\"snap.drag.start.\"+this.id,n||h||this,v,w,r)}if(!arguments.length){var v;return this.drag(function(a,b){this.attr({transform:v+(v?\"T\":\"t\")+[a,b]})},function(){v=this.transform().local})}this._drag={};r.push({el:this,start:u});this.mousedown(u);return this};\n",
       "A.undrag=function(){for(var b=r.length;b--;)r[b].el==this&&(this.unmousedown(r[b].start),r.splice(b,1),k.unbind(\"snap.drag.*.\"+this.id));!r.length&&a.unmousemove(e).unmouseup(l);return this}});C.plugin(function(a,v,y,C){y=y.prototype;var A=/^\\s*url\\((.+)\\)/,w=String,z=a._.$;a.filter={};y.filter=function(d){var f=this;\"svg\"!=f.type&&(f=f.paper);d=a.parse(w(d));var k=a._.id(),u=z(\"filter\");z(u,{id:k,filterUnits:\"userSpaceOnUse\"});u.appendChild(d.node);f.defs.appendChild(u);return new v(u)};k.on(\"snap.util.getattr.filter\",\n",
       "function(){k.stop();var d=z(this.node,\"filter\");if(d)return(d=w(d).match(A))&&a.select(d[1])});k.on(\"snap.util.attr.filter\",function(d){if(d instanceof v&&\"filter\"==d.type){k.stop();var f=d.node.id;f||(z(d.node,{id:d.id}),f=d.id);z(this.node,{filter:a.url(f)})}d&&\"none\"!=d||(k.stop(),this.node.removeAttribute(\"filter\"))});a.filter.blur=function(d,f){null==d&&(d=2);return a.format('<feGaussianBlur stdDeviation=\"{def}\"/>',{def:null==f?d:[d,f]})};a.filter.blur.toString=function(){return this()};a.filter.shadow=\n",
       "function(d,f,k,u,p){\"string\"==typeof k&&(p=u=k,k=4);\"string\"!=typeof u&&(p=u,u=\"#000\");null==k&&(k=4);null==p&&(p=1);null==d&&(d=0,f=2);null==f&&(f=d);u=a.color(u||\"#000\");return a.format('<feGaussianBlur in=\"SourceAlpha\" stdDeviation=\"{blur}\"/><feOffset dx=\"{dx}\" dy=\"{dy}\" result=\"offsetblur\"/><feFlood flood-color=\"{color}\"/><feComposite in2=\"offsetblur\" operator=\"in\"/><feComponentTransfer><feFuncA type=\"linear\" slope=\"{opacity}\"/></feComponentTransfer><feMerge><feMergeNode/><feMergeNode in=\"SourceGraphic\"/></feMerge>',\n",
       "{color:u,dx:d,dy:f,blur:k,opacity:p})};a.filter.shadow.toString=function(){return this()};a.filter.grayscale=function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"matrix\" values=\"{a} {b} {c} 0 0 {d} {e} {f} 0 0 {g} {b} {h} 0 0 0 0 0 1 0\"/>',{a:0.2126+0.7874*(1-d),b:0.7152-0.7152*(1-d),c:0.0722-0.0722*(1-d),d:0.2126-0.2126*(1-d),e:0.7152+0.2848*(1-d),f:0.0722-0.0722*(1-d),g:0.2126-0.2126*(1-d),h:0.0722+0.9278*(1-d)})};a.filter.grayscale.toString=function(){return this()};a.filter.sepia=\n",
       "function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"matrix\" values=\"{a} {b} {c} 0 0 {d} {e} {f} 0 0 {g} {h} {i} 0 0 0 0 0 1 0\"/>',{a:0.393+0.607*(1-d),b:0.769-0.769*(1-d),c:0.189-0.189*(1-d),d:0.349-0.349*(1-d),e:0.686+0.314*(1-d),f:0.168-0.168*(1-d),g:0.272-0.272*(1-d),h:0.534-0.534*(1-d),i:0.131+0.869*(1-d)})};a.filter.sepia.toString=function(){return this()};a.filter.saturate=function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"saturate\" values=\"{amount}\"/>',{amount:1-\n",
       "d})};a.filter.saturate.toString=function(){return this()};a.filter.hueRotate=function(d){return a.format('<feColorMatrix type=\"hueRotate\" values=\"{angle}\"/>',{angle:d||0})};a.filter.hueRotate.toString=function(){return this()};a.filter.invert=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"table\" tableValues=\"{amount} {amount2}\"/><feFuncG type=\"table\" tableValues=\"{amount} {amount2}\"/><feFuncB type=\"table\" tableValues=\"{amount} {amount2}\"/></feComponentTransfer>',{amount:d,\n",
       "amount2:1-d})};a.filter.invert.toString=function(){return this()};a.filter.brightness=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\"/><feFuncG type=\"linear\" slope=\"{amount}\"/><feFuncB type=\"linear\" slope=\"{amount}\"/></feComponentTransfer>',{amount:d})};a.filter.brightness.toString=function(){return this()};a.filter.contrast=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncG type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncB type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/></feComponentTransfer>',\n",
       "{amount:d,amount2:0.5-d/2})};a.filter.contrast.toString=function(){return this()}});return C});\n",
       "\n",
       "]]> </script>\n",
       "<script> <![CDATA[\n",
       "\n",
       "(function (glob, factory) {\n",
       "    // AMD support\n",
       "    if (typeof define === \"function\" && define.amd) {\n",
       "        // Define as an anonymous module\n",
       "        define(\"Gadfly\", [\"Snap.svg\"], function (Snap) {\n",
       "            return factory(Snap);\n",
       "        });\n",
       "    } else {\n",
       "        // Browser globals (glob is window)\n",
       "        // Snap adds itself to window\n",
       "        glob.Gadfly = factory(glob.Snap);\n",
       "    }\n",
       "}(this, function (Snap) {\n",
       "\n",
       "var Gadfly = {};\n",
       "\n",
       "// Get an x/y coordinate value in pixels\n",
       "var xPX = function(fig, x) {\n",
       "    var client_box = fig.node.getBoundingClientRect();\n",
       "    return x * fig.node.viewBox.baseVal.width / client_box.width;\n",
       "};\n",
       "\n",
       "var yPX = function(fig, y) {\n",
       "    var client_box = fig.node.getBoundingClientRect();\n",
       "    return y * fig.node.viewBox.baseVal.height / client_box.height;\n",
       "};\n",
       "\n",
       "\n",
       "Snap.plugin(function (Snap, Element, Paper, global) {\n",
       "    // Traverse upwards from a snap element to find and return the first\n",
       "    // note with the \"plotroot\" class.\n",
       "    Element.prototype.plotroot = function () {\n",
       "        var element = this;\n",
       "        while (!element.hasClass(\"plotroot\") && element.parent() != null) {\n",
       "            element = element.parent();\n",
       "        }\n",
       "        return element;\n",
       "    };\n",
       "\n",
       "    Element.prototype.svgroot = function () {\n",
       "        var element = this;\n",
       "        while (element.node.nodeName != \"svg\" && element.parent() != null) {\n",
       "            element = element.parent();\n",
       "        }\n",
       "        return element;\n",
       "    };\n",
       "\n",
       "    Element.prototype.plotbounds = function () {\n",
       "        var root = this.plotroot()\n",
       "        var bbox = root.select(\".guide.background\").node.getBBox();\n",
       "        return {\n",
       "            x0: bbox.x,\n",
       "            x1: bbox.x + bbox.width,\n",
       "            y0: bbox.y,\n",
       "            y1: bbox.y + bbox.height\n",
       "        };\n",
       "    };\n",
       "\n",
       "    Element.prototype.plotcenter = function () {\n",
       "        var root = this.plotroot()\n",
       "        var bbox = root.select(\".guide.background\").node.getBBox();\n",
       "        return {\n",
       "            x: bbox.x + bbox.width / 2,\n",
       "            y: bbox.y + bbox.height / 2\n",
       "        };\n",
       "    };\n",
       "\n",
       "    // Emulate IE style mouseenter/mouseleave events, since Microsoft always\n",
       "    // does everything right.\n",
       "    // See: http://www.dynamic-tools.net/toolbox/isMouseLeaveOrEnter/\n",
       "    var events = [\"mouseenter\", \"mouseleave\"];\n",
       "\n",
       "    for (i in events) {\n",
       "        (function (event_name) {\n",
       "            var event_name = events[i];\n",
       "            Element.prototype[event_name] = function (fn, scope) {\n",
       "                if (Snap.is(fn, \"function\")) {\n",
       "                    var fn2 = function (event) {\n",
       "                        if (event.type != \"mouseover\" && event.type != \"mouseout\") {\n",
       "                            return;\n",
       "                        }\n",
       "\n",
       "                        var reltg = event.relatedTarget ? event.relatedTarget :\n",
       "                            event.type == \"mouseout\" ? event.toElement : event.fromElement;\n",
       "                        while (reltg && reltg != this.node) reltg = reltg.parentNode;\n",
       "\n",
       "                        if (reltg != this.node) {\n",
       "                            return fn.apply(this, event);\n",
       "                        }\n",
       "                    };\n",
       "\n",
       "                    if (event_name == \"mouseenter\") {\n",
       "                        this.mouseover(fn2, scope);\n",
       "                    } else {\n",
       "                        this.mouseout(fn2, scope);\n",
       "                    }\n",
       "                }\n",
       "                return this;\n",
       "            };\n",
       "        })(events[i]);\n",
       "    }\n",
       "\n",
       "\n",
       "    Element.prototype.mousewheel = function (fn, scope) {\n",
       "        if (Snap.is(fn, \"function\")) {\n",
       "            var el = this;\n",
       "            var fn2 = function (event) {\n",
       "                fn.apply(el, [event]);\n",
       "            };\n",
       "        }\n",
       "\n",
       "        this.node.addEventListener(\n",
       "            /Firefox/i.test(navigator.userAgent) ? \"DOMMouseScroll\" : \"mousewheel\",\n",
       "            fn2);\n",
       "\n",
       "        return this;\n",
       "    };\n",
       "\n",
       "\n",
       "    // Snap's attr function can be too slow for things like panning/zooming.\n",
       "    // This is a function to directly update element attributes without going\n",
       "    // through eve.\n",
       "    Element.prototype.attribute = function(key, val) {\n",
       "        if (val === undefined) {\n",
       "            return this.node.getAttribute(key);\n",
       "        } else {\n",
       "            this.node.setAttribute(key, val);\n",
       "            return this;\n",
       "        }\n",
       "    };\n",
       "\n",
       "    Element.prototype.init_gadfly = function() {\n",
       "        this.mouseenter(Gadfly.plot_mouseover)\n",
       "            .mouseleave(Gadfly.plot_mouseout)\n",
       "            .dblclick(Gadfly.plot_dblclick)\n",
       "            .mousewheel(Gadfly.guide_background_scroll)\n",
       "            .drag(Gadfly.guide_background_drag_onmove,\n",
       "                  Gadfly.guide_background_drag_onstart,\n",
       "                  Gadfly.guide_background_drag_onend);\n",
       "        this.mouseenter(function (event) {\n",
       "            init_pan_zoom(this.plotroot());\n",
       "        });\n",
       "        return this;\n",
       "    };\n",
       "});\n",
       "\n",
       "\n",
       "// When the plot is moused over, emphasize the grid lines.\n",
       "Gadfly.plot_mouseover = function(event) {\n",
       "    var root = this.plotroot();\n",
       "\n",
       "    var keyboard_zoom = function(event) {\n",
       "        if (event.which == 187) { // plus\n",
       "            increase_zoom_by_position(root, 0.1, true);\n",
       "        } else if (event.which == 189) { // minus\n",
       "            increase_zoom_by_position(root, -0.1, true);\n",
       "        }\n",
       "    };\n",
       "    root.data(\"keyboard_zoom\", keyboard_zoom);\n",
       "    window.addEventListener(\"keyup\", keyboard_zoom);\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\"),\n",
       "        ygridlines = root.select(\".ygridlines\");\n",
       "\n",
       "    xgridlines.data(\"unfocused_strokedash\",\n",
       "                    xgridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
       "    ygridlines.data(\"unfocused_strokedash\",\n",
       "                    ygridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
       "\n",
       "    // emphasize grid lines\n",
       "    var destcolor = root.data(\"focused_xgrid_color\");\n",
       "    xgridlines.attribute(\"stroke-dasharray\", \"none\")\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"focused_ygrid_color\");\n",
       "    ygridlines.attribute(\"stroke-dasharray\", \"none\")\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    // reveal zoom slider\n",
       "    root.select(\".zoomslider\")\n",
       "        .animate({opacity: 1.0}, 250);\n",
       "};\n",
       "\n",
       "// Reset pan and zoom on double click\n",
       "Gadfly.plot_dblclick = function(event) {\n",
       "  set_plot_pan_zoom(this.plotroot(), 0.0, 0.0, 1.0);\n",
       "};\n",
       "\n",
       "// Unemphasize grid lines on mouse out.\n",
       "Gadfly.plot_mouseout = function(event) {\n",
       "    var root = this.plotroot();\n",
       "\n",
       "    window.removeEventListener(\"keyup\", root.data(\"keyboard_zoom\"));\n",
       "    root.data(\"keyboard_zoom\", undefined);\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\"),\n",
       "        ygridlines = root.select(\".ygridlines\");\n",
       "\n",
       "    var destcolor = root.data(\"unfocused_xgrid_color\");\n",
       "\n",
       "    xgridlines.attribute(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"unfocused_ygrid_color\");\n",
       "    ygridlines.attribute(\"stroke-dasharray\", ygridlines.data(\"unfocused_strokedash\"))\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    // hide zoom slider\n",
       "    root.select(\".zoomslider\")\n",
       "        .animate({opacity: 0.0}, 250);\n",
       "};\n",
       "\n",
       "\n",
       "var set_geometry_transform = function(root, tx, ty, scale) {\n",
       "    var xscalable = root.hasClass(\"xscalable\"),\n",
       "        yscalable = root.hasClass(\"yscalable\");\n",
       "\n",
       "    var old_scale = root.data(\"scale\");\n",
       "\n",
       "    var xscale = xscalable ? scale : 1.0,\n",
       "        yscale = yscalable ? scale : 1.0;\n",
       "\n",
       "    tx = xscalable ? tx : 0.0;\n",
       "    ty = yscalable ? ty : 0.0;\n",
       "\n",
       "    var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale);\n",
       "\n",
       "    root.selectAll(\".geometry, image\")\n",
       "        .forEach(function (element, i) {\n",
       "            element.transform(t);\n",
       "        });\n",
       "\n",
       "    bounds = root.plotbounds();\n",
       "\n",
       "    if (yscalable) {\n",
       "        var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale);\n",
       "        root.selectAll(\".xfixed\")\n",
       "            .forEach(function (element, i) {\n",
       "                element.transform(xfixed_t);\n",
       "            });\n",
       "\n",
       "        root.select(\".ylabels\")\n",
       "            .transform(xfixed_t)\n",
       "            .selectAll(\"text\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var cx = element.asPX(\"x\"),\n",
       "                        cy = element.asPX(\"y\");\n",
       "                    var st = element.data(\"static_transform\");\n",
       "                    unscale_t = new Snap.Matrix();\n",
       "                    unscale_t.scale(1, 1/scale, cx, cy).add(st);\n",
       "                    element.transform(unscale_t);\n",
       "\n",
       "                    var y = cy * scale + ty;\n",
       "                    element.attr(\"visibility\",\n",
       "                        bounds.y0 <= y && y <= bounds.y1 ? \"visible\" : \"hidden\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    if (xscalable) {\n",
       "        var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);\n",
       "        var xtrans = new Snap.Matrix().translate(tx, 0);\n",
       "        root.selectAll(\".yfixed\")\n",
       "            .forEach(function (element, i) {\n",
       "                element.transform(yfixed_t);\n",
       "            });\n",
       "\n",
       "        root.select(\".xlabels\")\n",
       "            .transform(yfixed_t)\n",
       "            .selectAll(\"text\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var cx = element.asPX(\"x\"),\n",
       "                        cy = element.asPX(\"y\");\n",
       "                    var st = element.data(\"static_transform\");\n",
       "                    unscale_t = new Snap.Matrix();\n",
       "                    unscale_t.scale(1/scale, 1, cx, cy).add(st);\n",
       "\n",
       "                    element.transform(unscale_t);\n",
       "\n",
       "                    var x = cx * scale + tx;\n",
       "                    element.attr(\"visibility\",\n",
       "                        bounds.x0 <= x && x <= bounds.x1 ? \"visible\" : \"hidden\");\n",
       "                    }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    // we must unscale anything that is scale invariance: widths, raiduses, etc.\n",
       "    var size_attribs = [\"font-size\"];\n",
       "    var unscaled_selection = \".geometry, .geometry *\";\n",
       "    if (xscalable) {\n",
       "        size_attribs.push(\"rx\");\n",
       "        unscaled_selection += \", .xgridlines\";\n",
       "    }\n",
       "    if (yscalable) {\n",
       "        size_attribs.push(\"ry\");\n",
       "        unscaled_selection += \", .ygridlines\";\n",
       "    }\n",
       "\n",
       "    root.selectAll(unscaled_selection)\n",
       "        .forEach(function (element, i) {\n",
       "            // circle need special help\n",
       "            if (element.node.nodeName == \"circle\") {\n",
       "                var cx = element.attribute(\"cx\"),\n",
       "                    cy = element.attribute(\"cy\");\n",
       "                unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n",
       "                                                        cx, cy);\n",
       "                element.transform(unscale_t);\n",
       "                return;\n",
       "            }\n",
       "\n",
       "            for (i in size_attribs) {\n",
       "                var key = size_attribs[i];\n",
       "                var val = parseFloat(element.attribute(key));\n",
       "                if (val !== undefined && val != 0 && !isNaN(val)) {\n",
       "                    element.attribute(key, val * old_scale / scale);\n",
       "                }\n",
       "            }\n",
       "        });\n",
       "};\n",
       "\n",
       "\n",
       "// Find the most appropriate tick scale and update label visibility.\n",
       "var update_tickscale = function(root, scale, axis) {\n",
       "    if (!root.hasClass(axis + \"scalable\")) return;\n",
       "\n",
       "    var tickscales = root.data(axis + \"tickscales\");\n",
       "    var best_tickscale = 1.0;\n",
       "    var best_tickscale_dist = Infinity;\n",
       "    for (tickscale in tickscales) {\n",
       "        var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n",
       "        if (dist < best_tickscale_dist) {\n",
       "            best_tickscale_dist = dist;\n",
       "            best_tickscale = tickscale;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    if (best_tickscale != root.data(axis + \"tickscale\")) {\n",
       "        root.data(axis + \"tickscale\", best_tickscale);\n",
       "        var mark_inscale_gridlines = function (element, i) {\n",
       "            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
       "            element.attribute(\"gadfly:inscale\", inscale);\n",
       "            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
       "        };\n",
       "\n",
       "        var mark_inscale_labels = function (element, i) {\n",
       "            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
       "            element.attribute(\"gadfly:inscale\", inscale);\n",
       "            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
       "        };\n",
       "\n",
       "        root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n",
       "        root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "var set_plot_pan_zoom = function(root, tx, ty, scale) {\n",
       "    var old_scale = root.data(\"scale\");\n",
       "    var bounds = root.plotbounds();\n",
       "\n",
       "    var width = bounds.x1 - bounds.x0,\n",
       "        height = bounds.y1 - bounds.y0;\n",
       "\n",
       "    // compute the viewport derived from tx, ty, and scale\n",
       "    var x_min = -width * scale - (scale * width - width),\n",
       "        x_max = width * scale,\n",
       "        y_min = -height * scale - (scale * height - height),\n",
       "        y_max = height * scale;\n",
       "\n",
       "    var x0 = bounds.x0 - scale * bounds.x0,\n",
       "        y0 = bounds.y0 - scale * bounds.y0;\n",
       "\n",
       "    var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n",
       "        ty = Math.max(Math.min(ty - y0, y_max), y_min);\n",
       "\n",
       "    tx += x0;\n",
       "    ty += y0;\n",
       "\n",
       "    // when the scale change, we may need to alter which set of\n",
       "    // ticks is being displayed\n",
       "    if (scale != old_scale) {\n",
       "        update_tickscale(root, scale, \"x\");\n",
       "        update_tickscale(root, scale, \"y\");\n",
       "    }\n",
       "\n",
       "    set_geometry_transform(root, tx, ty, scale);\n",
       "\n",
       "    root.data(\"scale\", scale);\n",
       "    root.data(\"tx\", tx);\n",
       "    root.data(\"ty\", ty);\n",
       "};\n",
       "\n",
       "\n",
       "var scale_centered_translation = function(root, scale) {\n",
       "    var bounds = root.plotbounds();\n",
       "\n",
       "    var width = bounds.x1 - bounds.x0,\n",
       "        height = bounds.y1 - bounds.y0;\n",
       "\n",
       "    var tx0 = root.data(\"tx\"),\n",
       "        ty0 = root.data(\"ty\");\n",
       "\n",
       "    var scale0 = root.data(\"scale\");\n",
       "\n",
       "    // how off from center the current view is\n",
       "    var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n",
       "        yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n",
       "\n",
       "    // rescale offsets\n",
       "    xoff = xoff * scale / scale0;\n",
       "    yoff = yoff * scale / scale0;\n",
       "\n",
       "    // adjust for the panel position being scaled\n",
       "    var x_edge_adjust = bounds.x0 * (1 - scale),\n",
       "        y_edge_adjust = bounds.y0 * (1 - scale);\n",
       "\n",
       "    return {\n",
       "        x: xoff + x_edge_adjust + (width - width * scale) / 2,\n",
       "        y: yoff + y_edge_adjust + (height - height * scale) / 2\n",
       "    };\n",
       "};\n",
       "\n",
       "\n",
       "// Initialize data for panning zooming if it isn't already.\n",
       "var init_pan_zoom = function(root) {\n",
       "    if (root.data(\"zoompan-ready\")) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    // The non-scaling-stroke trick. Rather than try to correct for the\n",
       "    // stroke-width when zooming, we force it to a fixed value.\n",
       "    var px_per_mm = root.node.getCTM().a;\n",
       "\n",
       "    // Drag events report deltas in pixels, which we'd like to convert to\n",
       "    // millimeters.\n",
       "    root.data(\"px_per_mm\", px_per_mm);\n",
       "\n",
       "    root.selectAll(\"path\")\n",
       "        .forEach(function (element, i) {\n",
       "        sw = element.asPX(\"stroke-width\") * px_per_mm;\n",
       "        if (sw > 0) {\n",
       "            element.attribute(\"stroke-width\", sw);\n",
       "            element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n",
       "        }\n",
       "    });\n",
       "\n",
       "    // Store ticks labels original tranformation\n",
       "    root.selectAll(\".xlabels > text, .ylabels > text\")\n",
       "        .forEach(function (element, i) {\n",
       "            var lm = element.transform().localMatrix;\n",
       "            element.data(\"static_transform\",\n",
       "                new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n",
       "        });\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\");\n",
       "    var ygridlines = root.select(\".ygridlines\");\n",
       "    var xlabels = root.select(\".xlabels\");\n",
       "    var ylabels = root.select(\".ylabels\");\n",
       "\n",
       "    if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n",
       "    if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n",
       "    if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n",
       "    if (root.data(\"xtickscales\") === undefined) {\n",
       "\n",
       "        // index all the tick scales that are listed\n",
       "        var xtickscales = {};\n",
       "        var ytickscales = {};\n",
       "        var add_x_tick_scales = function (element, i) {\n",
       "            xtickscales[element.attribute(\"gadfly:scale\")] = true;\n",
       "        };\n",
       "        var add_y_tick_scales = function (element, i) {\n",
       "            ytickscales[element.attribute(\"gadfly:scale\")] = true;\n",
       "        };\n",
       "\n",
       "        if (xgridlines) xgridlines.selectAll(\"path\").forEach(add_x_tick_scales);\n",
       "        if (ygridlines) ygridlines.selectAll(\"path\").forEach(add_y_tick_scales);\n",
       "        if (xlabels) xlabels.selectAll(\"text\").forEach(add_x_tick_scales);\n",
       "        if (ylabels) ylabels.selectAll(\"text\").forEach(add_y_tick_scales);\n",
       "\n",
       "        root.data(\"xtickscales\", xtickscales);\n",
       "        root.data(\"ytickscales\", ytickscales);\n",
       "        root.data(\"xtickscale\", 1.0);\n",
       "    }\n",
       "\n",
       "    var min_scale = 1.0, max_scale = 1.0;\n",
       "    for (scale in xtickscales) {\n",
       "        min_scale = Math.min(min_scale, scale);\n",
       "        max_scale = Math.max(max_scale, scale);\n",
       "    }\n",
       "    for (scale in ytickscales) {\n",
       "        min_scale = Math.min(min_scale, scale);\n",
       "        max_scale = Math.max(max_scale, scale);\n",
       "    }\n",
       "    root.data(\"min_scale\", min_scale);\n",
       "    root.data(\"max_scale\", max_scale);\n",
       "\n",
       "    // store the original positions of labels\n",
       "    if (xlabels) {\n",
       "        xlabels.selectAll(\"text\")\n",
       "               .forEach(function (element, i) {\n",
       "                   element.data(\"x\", element.asPX(\"x\"));\n",
       "               });\n",
       "    }\n",
       "\n",
       "    if (ylabels) {\n",
       "        ylabels.selectAll(\"text\")\n",
       "               .forEach(function (element, i) {\n",
       "                   element.data(\"y\", element.asPX(\"y\"));\n",
       "               });\n",
       "    }\n",
       "\n",
       "    // mark grid lines and ticks as in or out of scale.\n",
       "    var mark_inscale = function (element, i) {\n",
       "        element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n",
       "    };\n",
       "\n",
       "    if (xgridlines) xgridlines.selectAll(\"path\").forEach(mark_inscale);\n",
       "    if (ygridlines) ygridlines.selectAll(\"path\").forEach(mark_inscale);\n",
       "    if (xlabels) xlabels.selectAll(\"text\").forEach(mark_inscale);\n",
       "    if (ylabels) ylabels.selectAll(\"text\").forEach(mark_inscale);\n",
       "\n",
       "    // figure out the upper ond lower bounds on panning using the maximum\n",
       "    // and minum grid lines\n",
       "    var bounds = root.plotbounds();\n",
       "    var pan_bounds = {\n",
       "        x0: 0.0,\n",
       "        y0: 0.0,\n",
       "        x1: 0.0,\n",
       "        y1: 0.0\n",
       "    };\n",
       "\n",
       "    if (xgridlines) {\n",
       "        xgridlines\n",
       "            .selectAll(\"path\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var bbox = element.node.getBBox();\n",
       "                    if (bounds.x1 - bbox.x < pan_bounds.x0) {\n",
       "                        pan_bounds.x0 = bounds.x1 - bbox.x;\n",
       "                    }\n",
       "                    if (bounds.x0 - bbox.x > pan_bounds.x1) {\n",
       "                        pan_bounds.x1 = bounds.x0 - bbox.x;\n",
       "                    }\n",
       "                    element.attr(\"visibility\", \"visible\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    if (ygridlines) {\n",
       "        ygridlines\n",
       "            .selectAll(\"path\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var bbox = element.node.getBBox();\n",
       "                    if (bounds.y1 - bbox.y < pan_bounds.y0) {\n",
       "                        pan_bounds.y0 = bounds.y1 - bbox.y;\n",
       "                    }\n",
       "                    if (bounds.y0 - bbox.y > pan_bounds.y1) {\n",
       "                        pan_bounds.y1 = bounds.y0 - bbox.y;\n",
       "                    }\n",
       "                    element.attr(\"visibility\", \"visible\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    // nudge these values a little\n",
       "    pan_bounds.x0 -= 5;\n",
       "    pan_bounds.x1 += 5;\n",
       "    pan_bounds.y0 -= 5;\n",
       "    pan_bounds.y1 += 5;\n",
       "    root.data(\"pan_bounds\", pan_bounds);\n",
       "\n",
       "    root.data(\"zoompan-ready\", true)\n",
       "};\n",
       "\n",
       "\n",
       "// drag actions, i.e. zooming and panning\n",
       "var pan_action = {\n",
       "    start: function(root, x, y, event) {\n",
       "        root.data(\"dx\", 0);\n",
       "        root.data(\"dy\", 0);\n",
       "        root.data(\"tx0\", root.data(\"tx\"));\n",
       "        root.data(\"ty0\", root.data(\"ty\"));\n",
       "    },\n",
       "    update: function(root, dx, dy, x, y, event) {\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\n",
       "        dx /= px_per_mm;\n",
       "        dy /= px_per_mm;\n",
       "\n",
       "        var tx0 = root.data(\"tx\"),\n",
       "            ty0 = root.data(\"ty\");\n",
       "\n",
       "        var dx0 = root.data(\"dx\"),\n",
       "            dy0 = root.data(\"dy\");\n",
       "\n",
       "        root.data(\"dx\", dx);\n",
       "        root.data(\"dy\", dy);\n",
       "\n",
       "        dx = dx - dx0;\n",
       "        dy = dy - dy0;\n",
       "\n",
       "        var tx = tx0 + dx,\n",
       "            ty = ty0 + dy;\n",
       "\n",
       "        set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n",
       "    },\n",
       "    end: function(root, event) {\n",
       "\n",
       "    },\n",
       "    cancel: function(root) {\n",
       "        set_plot_pan_zoom(root, root.data(\"tx0\"), root.data(\"ty0\"), root.data(\"scale\"));\n",
       "    }\n",
       "};\n",
       "\n",
       "var zoom_box;\n",
       "var zoom_action = {\n",
       "    start: function(root, x, y, event) {\n",
       "        var bounds = root.plotbounds();\n",
       "        var width = bounds.x1 - bounds.x0,\n",
       "            height = bounds.y1 - bounds.y0;\n",
       "        var ratio = width / height;\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\n",
       "            yscalable = root.hasClass(\"yscalable\");\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\n",
       "        x = xscalable ? x / px_per_mm : bounds.x0;\n",
       "        y = yscalable ? y / px_per_mm : bounds.y0;\n",
       "        var w = xscalable ? 0 : width;\n",
       "        var h = yscalable ? 0 : height;\n",
       "        zoom_box = root.rect(x, y, w, h).attr({\n",
       "            \"fill\": \"#000\",\n",
       "            \"opacity\": 0.25\n",
       "        });\n",
       "        zoom_box.data(\"ratio\", ratio);\n",
       "    },\n",
       "    update: function(root, dx, dy, x, y, event) {\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\n",
       "            yscalable = root.hasClass(\"yscalable\");\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\n",
       "        var bounds = root.plotbounds();\n",
       "        if (yscalable) {\n",
       "            y /= px_per_mm;\n",
       "            y = Math.max(bounds.y0, y);\n",
       "            y = Math.min(bounds.y1, y);\n",
       "        } else {\n",
       "            y = bounds.y1;\n",
       "        }\n",
       "        if (xscalable) {\n",
       "            x /= px_per_mm;\n",
       "            x = Math.max(bounds.x0, x);\n",
       "            x = Math.min(bounds.x1, x);\n",
       "        } else {\n",
       "            x = bounds.x1;\n",
       "        }\n",
       "\n",
       "        dx = x - zoom_box.attr(\"x\");\n",
       "        dy = y - zoom_box.attr(\"y\");\n",
       "        if (xscalable && yscalable) {\n",
       "            var ratio = zoom_box.data(\"ratio\");\n",
       "            var width = Math.min(Math.abs(dx), ratio * Math.abs(dy));\n",
       "            var height = Math.min(Math.abs(dy), Math.abs(dx) / ratio);\n",
       "            dx = width * dx / Math.abs(dx);\n",
       "            dy = height * dy / Math.abs(dy);\n",
       "        }\n",
       "        var xoffset = 0,\n",
       "            yoffset = 0;\n",
       "        if (dx < 0) {\n",
       "            xoffset = dx;\n",
       "            dx = -1 * dx;\n",
       "        }\n",
       "        if (dy < 0) {\n",
       "            yoffset = dy;\n",
       "            dy = -1 * dy;\n",
       "        }\n",
       "        if (isNaN(dy)) {\n",
       "            dy = 0.0;\n",
       "        }\n",
       "        if (isNaN(dx)) {\n",
       "            dx = 0.0;\n",
       "        }\n",
       "        zoom_box.transform(\"T\" + xoffset + \",\" + yoffset);\n",
       "        zoom_box.attr(\"width\", dx);\n",
       "        zoom_box.attr(\"height\", dy);\n",
       "    },\n",
       "    end: function(root, event) {\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\n",
       "            yscalable = root.hasClass(\"yscalable\");\n",
       "        var zoom_bounds = zoom_box.getBBox();\n",
       "        if (zoom_bounds.width * zoom_bounds.height <= 0) {\n",
       "            return;\n",
       "        }\n",
       "        var plot_bounds = root.plotbounds();\n",
       "        var zoom_factor = 1.0;\n",
       "        if (yscalable) {\n",
       "            zoom_factor = (plot_bounds.y1 - plot_bounds.y0) / zoom_bounds.height;\n",
       "        } else {\n",
       "            zoom_factor = (plot_bounds.x1 - plot_bounds.x0) / zoom_bounds.width;\n",
       "        }\n",
       "        var tx = (root.data(\"tx\") - zoom_bounds.x) * zoom_factor + plot_bounds.x0,\n",
       "            ty = (root.data(\"ty\") - zoom_bounds.y) * zoom_factor + plot_bounds.y0;\n",
       "        set_plot_pan_zoom(root, tx, ty, root.data(\"scale\") * zoom_factor);\n",
       "        zoom_box.remove();\n",
       "    },\n",
       "    cancel: function(root) {\n",
       "        zoom_box.remove();\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onstart = function(x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var scalable = root.hasClass(\"xscalable\") || root.hasClass(\"yscalable\");\n",
       "    var zoomable = !event.altKey && !event.ctrlKey && event.shiftKey && scalable;\n",
       "    var panable = !event.altKey && !event.ctrlKey && !event.shiftKey && scalable;\n",
       "    var drag_action = zoomable ? zoom_action :\n",
       "                      panable  ? pan_action :\n",
       "                                 undefined;\n",
       "    root.data(\"drag_action\", drag_action);\n",
       "    if (drag_action) {\n",
       "        var cancel_drag_action = function(event) {\n",
       "            if (event.which == 27) { // esc key\n",
       "                drag_action.cancel(root);\n",
       "                root.data(\"drag_action\", undefined);\n",
       "            }\n",
       "        };\n",
       "        window.addEventListener(\"keyup\", cancel_drag_action);\n",
       "        root.data(\"cancel_drag_action\", cancel_drag_action);\n",
       "        drag_action.start(root, x, y, event);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var drag_action = root.data(\"drag_action\");\n",
       "    if (drag_action) {\n",
       "        drag_action.update(root, dx, dy, x, y, event);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onend = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    window.removeEventListener(\"keyup\", root.data(\"cancel_drag_action\"));\n",
       "    root.data(\"cancel_drag_action\", undefined);\n",
       "    var drag_action = root.data(\"drag_action\");\n",
       "    if (drag_action) {\n",
       "        drag_action.end(root, event);\n",
       "    }\n",
       "    root.data(\"drag_action\", undefined);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_scroll = function(event) {\n",
       "    if (event.shiftKey) {\n",
       "        increase_zoom_by_position(this.plotroot(), 0.001 * event.wheelDelta);\n",
       "        event.preventDefault();\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_button_mouseover = function(event) {\n",
       "    this.select(\".button_logo\")\n",
       "         .animate({fill: this.data(\"mouseover_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_button_mouseout = function(event) {\n",
       "     this.select(\".button_logo\")\n",
       "         .animate({fill: this.data(\"mouseout_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_zoomout_click = function(event) {\n",
       "    increase_zoom_by_position(this.plotroot(), -0.1, true);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_zoomin_click = function(event) {\n",
       "    increase_zoom_by_position(this.plotroot(), 0.1, true);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_track_click = function(event) {\n",
       "    // TODO\n",
       "};\n",
       "\n",
       "\n",
       "// Map slider position x to scale y using the function y = a*exp(b*x)+c.\n",
       "// The constants a, b, and c are solved using the constraint that the function\n",
       "// should go through the points (0; min_scale), (0.5; 1), and (1; max_scale).\n",
       "var scale_from_slider_position = function(position, min_scale, max_scale) {\n",
       "    var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
       "        b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
       "        c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
       "    return a * Math.exp(b * position) + c;\n",
       "}\n",
       "\n",
       "// inverse of scale_from_slider_position\n",
       "var slider_position_from_scale = function(scale, min_scale, max_scale) {\n",
       "    var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
       "        b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
       "        c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
       "    return 1 / b * Math.log((scale - c) / a);\n",
       "}\n",
       "\n",
       "var increase_zoom_by_position = function(root, delta_position, animate) {\n",
       "    var scale = root.data(\"scale\"),\n",
       "        min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\");\n",
       "    var position = slider_position_from_scale(scale, min_scale, max_scale);\n",
       "    position += delta_position;\n",
       "    scale = scale_from_slider_position(position, min_scale, max_scale);\n",
       "    set_zoom(root, scale, animate);\n",
       "}\n",
       "\n",
       "var set_zoom = function(root, scale, animate) {\n",
       "    var min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\"),\n",
       "        old_scale = root.data(\"scale\");\n",
       "    var new_scale = Math.max(min_scale, Math.min(scale, max_scale));\n",
       "    if (animate) {\n",
       "        Snap.animate(\n",
       "            old_scale,\n",
       "            new_scale,\n",
       "            function (new_scale) {\n",
       "                update_plot_scale(root, new_scale);\n",
       "            },\n",
       "            200);\n",
       "    } else {\n",
       "        update_plot_scale(root, new_scale);\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "var update_plot_scale = function(root, new_scale) {\n",
       "    var trans = scale_centered_translation(root, new_scale);\n",
       "    set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n",
       "\n",
       "    root.selectAll(\".zoomslider_thumb\")\n",
       "        .forEach(function (element, i) {\n",
       "            var min_pos = element.data(\"min_pos\"),\n",
       "                max_pos = element.data(\"max_pos\"),\n",
       "                min_scale = root.data(\"min_scale\"),\n",
       "                max_scale = root.data(\"max_scale\");\n",
       "            var xmid = (min_pos + max_pos) / 2;\n",
       "            var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n",
       "            element.transform(new Snap.Matrix().translate(\n",
       "                Math.max(min_pos, Math.min(\n",
       "                         max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n",
       "    });\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var min_pos = this.data(\"min_pos\"),\n",
       "        max_pos = this.data(\"max_pos\"),\n",
       "        min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\"),\n",
       "        old_scale = root.data(\"old_scale\");\n",
       "\n",
       "    var px_per_mm = root.data(\"px_per_mm\");\n",
       "    dx /= px_per_mm;\n",
       "    dy /= px_per_mm;\n",
       "\n",
       "    var xmid = (min_pos + max_pos) / 2;\n",
       "    var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n",
       "                   dx / (max_pos - min_pos);\n",
       "\n",
       "    // compute the new scale\n",
       "    var new_scale = scale_from_slider_position(xpos, min_scale, max_scale);\n",
       "    new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n",
       "\n",
       "    update_plot_scale(root, new_scale);\n",
       "    event.stopPropagation();\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragstart = function(x, y, event) {\n",
       "    this.animate({fill: this.data(\"mouseover_color\")}, 100);\n",
       "    var root = this.plotroot();\n",
       "\n",
       "    // keep track of what the scale was when we started dragging\n",
       "    root.data(\"old_scale\", root.data(\"scale\"));\n",
       "    event.stopPropagation();\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragend = function(event) {\n",
       "    this.animate({fill: this.data(\"mouseout_color\")}, 100);\n",
       "    event.stopPropagation();\n",
       "};\n",
       "\n",
       "\n",
       "var toggle_color_class = function(root, color_class, ison) {\n",
       "    var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n",
       "    var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n",
       "    if (ison) {\n",
       "        guides.animate({opacity: 0.5}, 250);\n",
       "        geoms.animate({opacity: 0.0}, 250);\n",
       "    } else {\n",
       "        guides.animate({opacity: 1.0}, 250);\n",
       "        geoms.animate({opacity: 1.0}, 250);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.colorkey_swatch_click = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    var color_class = this.data(\"color_class\");\n",
       "\n",
       "    if (event.shiftKey) {\n",
       "        root.selectAll(\".colorkey text\")\n",
       "            .forEach(function (element) {\n",
       "                var other_color_class = element.data(\"color_class\");\n",
       "                if (other_color_class != color_class) {\n",
       "                    toggle_color_class(root, other_color_class,\n",
       "                                       element.attr(\"opacity\") == 1.0);\n",
       "                }\n",
       "            });\n",
       "    } else {\n",
       "        toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "return Gadfly;\n",
       "\n",
       "}));\n",
       "\n",
       "\n",
       "//@ sourceURL=gadfly.js\n",
       "\n",
       "(function (glob, factory) {\n",
       "    // AMD support\n",
       "      if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n",
       "        require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n",
       "            factory(Snap, Gadfly);\n",
       "        });\n",
       "      } else {\n",
       "          factory(glob.Snap, glob.Gadfly);\n",
       "      }\n",
       "})(window, function (Snap, Gadfly) {\n",
       "    var fig = Snap(\"#img-7242ccce\");\n",
       "fig.select(\"#img-7242ccce-5\")\n",
       "   .init_gadfly();\n",
       "fig.select(\"#img-7242ccce-7\")\n",
       "   .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#img-7242ccce-7\")\n",
       "   .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#img-7242ccce-8\")\n",
       "   .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#img-7242ccce-8\")\n",
       "   .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#img-7242ccce-36\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#img-7242ccce-36\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#img-7242ccce-36\")\n",
       "   .click(Gadfly.zoomslider_zoomin_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "fig.select(\"#img-7242ccce-38\")\n",
       "   .data(\"max_pos\", 120.42)\n",
       ";\n",
       "fig.select(\"#img-7242ccce-38\")\n",
       "   .data(\"min_pos\", 103.42)\n",
       ";\n",
       "fig.select(\"#img-7242ccce-38\")\n",
       "   .click(Gadfly.zoomslider_track_click);\n",
       "fig.select(\"#img-7242ccce-39\")\n",
       "   .data(\"max_pos\", 120.42)\n",
       ";\n",
       "fig.select(\"#img-7242ccce-39\")\n",
       "   .data(\"min_pos\", 103.42)\n",
       ";\n",
       "fig.select(\"#img-7242ccce-39\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#img-7242ccce-39\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#img-7242ccce-39\")\n",
       "   .drag(Gadfly.zoomslider_thumb_dragmove,\n",
       "     Gadfly.zoomslider_thumb_dragstart,\n",
       "     Gadfly.zoomslider_thumb_dragend)\n",
       ";\n",
       "fig.select(\"#img-7242ccce-40\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#img-7242ccce-40\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#img-7242ccce-40\")\n",
       "   .click(Gadfly.zoomslider_zoomout_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "    });\n",
       "]]> </script>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "Plot(...)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using Gadfly\n",
    "\n",
    "plot(\n",
    "    layer(x=xcoarse,y=ycoarse,Geom.point),\n",
    "    layer(x=xfine,y=yconst,Geom.line,Theme(default_color=colorant\"magenta\")),\n",
    "    layer(x=xfine,y=ylinear,Geom.line,Theme(default_color=colorant\"red\")),\n",
    "    layer(x=xfine,y=yquadratic,Geom.line,Theme(default_color=colorant\"green\"))\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2D interpolation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Interpolations.jl` supports interpolation in arbitrary dimensions - just give it an `Array{T,N}` where elements `t::T` support operations like `a::Real * t + b::Real * t` and it will figure it out. However, since it starts getting difficult to visualize higher-dimensional functions, we'll stick with an example in 2D, namely the function $g(x,y) = x^2 \\cdot \\sin(y)$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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       "\n",
       "]]> </script>\n",
       "<script> <![CDATA[\n",
       "\n",
       "(function (glob, factory) {\n",
       "    // AMD support\n",
       "    if (typeof define === \"function\" && define.amd) {\n",
       "        // Define as an anonymous module\n",
       "        define(\"Gadfly\", [\"Snap.svg\"], function (Snap) {\n",
       "            return factory(Snap);\n",
       "        });\n",
       "    } else {\n",
       "        // Browser globals (glob is window)\n",
       "        // Snap adds itself to window\n",
       "        glob.Gadfly = factory(glob.Snap);\n",
       "    }\n",
       "}(this, function (Snap) {\n",
       "\n",
       "var Gadfly = {};\n",
       "\n",
       "// Get an x/y coordinate value in pixels\n",
       "var xPX = function(fig, x) {\n",
       "    var client_box = fig.node.getBoundingClientRect();\n",
       "    return x * fig.node.viewBox.baseVal.width / client_box.width;\n",
       "};\n",
       "\n",
       "var yPX = function(fig, y) {\n",
       "    var client_box = fig.node.getBoundingClientRect();\n",
       "    return y * fig.node.viewBox.baseVal.height / client_box.height;\n",
       "};\n",
       "\n",
       "\n",
       "Snap.plugin(function (Snap, Element, Paper, global) {\n",
       "    // Traverse upwards from a snap element to find and return the first\n",
       "    // note with the \"plotroot\" class.\n",
       "    Element.prototype.plotroot = function () {\n",
       "        var element = this;\n",
       "        while (!element.hasClass(\"plotroot\") && element.parent() != null) {\n",
       "            element = element.parent();\n",
       "        }\n",
       "        return element;\n",
       "    };\n",
       "\n",
       "    Element.prototype.svgroot = function () {\n",
       "        var element = this;\n",
       "        while (element.node.nodeName != \"svg\" && element.parent() != null) {\n",
       "            element = element.parent();\n",
       "        }\n",
       "        return element;\n",
       "    };\n",
       "\n",
       "    Element.prototype.plotbounds = function () {\n",
       "        var root = this.plotroot()\n",
       "        var bbox = root.select(\".guide.background\").node.getBBox();\n",
       "        return {\n",
       "            x0: bbox.x,\n",
       "            x1: bbox.x + bbox.width,\n",
       "            y0: bbox.y,\n",
       "            y1: bbox.y + bbox.height\n",
       "        };\n",
       "    };\n",
       "\n",
       "    Element.prototype.plotcenter = function () {\n",
       "        var root = this.plotroot()\n",
       "        var bbox = root.select(\".guide.background\").node.getBBox();\n",
       "        return {\n",
       "            x: bbox.x + bbox.width / 2,\n",
       "            y: bbox.y + bbox.height / 2\n",
       "        };\n",
       "    };\n",
       "\n",
       "    // Emulate IE style mouseenter/mouseleave events, since Microsoft always\n",
       "    // does everything right.\n",
       "    // See: http://www.dynamic-tools.net/toolbox/isMouseLeaveOrEnter/\n",
       "    var events = [\"mouseenter\", \"mouseleave\"];\n",
       "\n",
       "    for (i in events) {\n",
       "        (function (event_name) {\n",
       "            var event_name = events[i];\n",
       "            Element.prototype[event_name] = function (fn, scope) {\n",
       "                if (Snap.is(fn, \"function\")) {\n",
       "                    var fn2 = function (event) {\n",
       "                        if (event.type != \"mouseover\" && event.type != \"mouseout\") {\n",
       "                            return;\n",
       "                        }\n",
       "\n",
       "                        var reltg = event.relatedTarget ? event.relatedTarget :\n",
       "                            event.type == \"mouseout\" ? event.toElement : event.fromElement;\n",
       "                        while (reltg && reltg != this.node) reltg = reltg.parentNode;\n",
       "\n",
       "                        if (reltg != this.node) {\n",
       "                            return fn.apply(this, event);\n",
       "                        }\n",
       "                    };\n",
       "\n",
       "                    if (event_name == \"mouseenter\") {\n",
       "                        this.mouseover(fn2, scope);\n",
       "                    } else {\n",
       "                        this.mouseout(fn2, scope);\n",
       "                    }\n",
       "                }\n",
       "                return this;\n",
       "            };\n",
       "        })(events[i]);\n",
       "    }\n",
       "\n",
       "\n",
       "    Element.prototype.mousewheel = function (fn, scope) {\n",
       "        if (Snap.is(fn, \"function\")) {\n",
       "            var el = this;\n",
       "            var fn2 = function (event) {\n",
       "                fn.apply(el, [event]);\n",
       "            };\n",
       "        }\n",
       "\n",
       "        this.node.addEventListener(\n",
       "            /Firefox/i.test(navigator.userAgent) ? \"DOMMouseScroll\" : \"mousewheel\",\n",
       "            fn2);\n",
       "\n",
       "        return this;\n",
       "    };\n",
       "\n",
       "\n",
       "    // Snap's attr function can be too slow for things like panning/zooming.\n",
       "    // This is a function to directly update element attributes without going\n",
       "    // through eve.\n",
       "    Element.prototype.attribute = function(key, val) {\n",
       "        if (val === undefined) {\n",
       "            return this.node.getAttribute(key);\n",
       "        } else {\n",
       "            this.node.setAttribute(key, val);\n",
       "            return this;\n",
       "        }\n",
       "    };\n",
       "\n",
       "    Element.prototype.init_gadfly = function() {\n",
       "        this.mouseenter(Gadfly.plot_mouseover)\n",
       "            .mouseleave(Gadfly.plot_mouseout)\n",
       "            .dblclick(Gadfly.plot_dblclick)\n",
       "            .mousewheel(Gadfly.guide_background_scroll)\n",
       "            .drag(Gadfly.guide_background_drag_onmove,\n",
       "                  Gadfly.guide_background_drag_onstart,\n",
       "                  Gadfly.guide_background_drag_onend);\n",
       "        this.mouseenter(function (event) {\n",
       "            init_pan_zoom(this.plotroot());\n",
       "        });\n",
       "        return this;\n",
       "    };\n",
       "});\n",
       "\n",
       "\n",
       "// When the plot is moused over, emphasize the grid lines.\n",
       "Gadfly.plot_mouseover = function(event) {\n",
       "    var root = this.plotroot();\n",
       "\n",
       "    var keyboard_zoom = function(event) {\n",
       "        if (event.which == 187) { // plus\n",
       "            increase_zoom_by_position(root, 0.1, true);\n",
       "        } else if (event.which == 189) { // minus\n",
       "            increase_zoom_by_position(root, -0.1, true);\n",
       "        }\n",
       "    };\n",
       "    root.data(\"keyboard_zoom\", keyboard_zoom);\n",
       "    window.addEventListener(\"keyup\", keyboard_zoom);\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\"),\n",
       "        ygridlines = root.select(\".ygridlines\");\n",
       "\n",
       "    xgridlines.data(\"unfocused_strokedash\",\n",
       "                    xgridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
       "    ygridlines.data(\"unfocused_strokedash\",\n",
       "                    ygridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
       "\n",
       "    // emphasize grid lines\n",
       "    var destcolor = root.data(\"focused_xgrid_color\");\n",
       "    xgridlines.attribute(\"stroke-dasharray\", \"none\")\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"focused_ygrid_color\");\n",
       "    ygridlines.attribute(\"stroke-dasharray\", \"none\")\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    // reveal zoom slider\n",
       "    root.select(\".zoomslider\")\n",
       "        .animate({opacity: 1.0}, 250);\n",
       "};\n",
       "\n",
       "// Reset pan and zoom on double click\n",
       "Gadfly.plot_dblclick = function(event) {\n",
       "  set_plot_pan_zoom(this.plotroot(), 0.0, 0.0, 1.0);\n",
       "};\n",
       "\n",
       "// Unemphasize grid lines on mouse out.\n",
       "Gadfly.plot_mouseout = function(event) {\n",
       "    var root = this.plotroot();\n",
       "\n",
       "    window.removeEventListener(\"keyup\", root.data(\"keyboard_zoom\"));\n",
       "    root.data(\"keyboard_zoom\", undefined);\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\"),\n",
       "        ygridlines = root.select(\".ygridlines\");\n",
       "\n",
       "    var destcolor = root.data(\"unfocused_xgrid_color\");\n",
       "\n",
       "    xgridlines.attribute(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"unfocused_ygrid_color\");\n",
       "    ygridlines.attribute(\"stroke-dasharray\", ygridlines.data(\"unfocused_strokedash\"))\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    // hide zoom slider\n",
       "    root.select(\".zoomslider\")\n",
       "        .animate({opacity: 0.0}, 250);\n",
       "};\n",
       "\n",
       "\n",
       "var set_geometry_transform = function(root, tx, ty, scale) {\n",
       "    var xscalable = root.hasClass(\"xscalable\"),\n",
       "        yscalable = root.hasClass(\"yscalable\");\n",
       "\n",
       "    var old_scale = root.data(\"scale\");\n",
       "\n",
       "    var xscale = xscalable ? scale : 1.0,\n",
       "        yscale = yscalable ? scale : 1.0;\n",
       "\n",
       "    tx = xscalable ? tx : 0.0;\n",
       "    ty = yscalable ? ty : 0.0;\n",
       "\n",
       "    var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale);\n",
       "\n",
       "    root.selectAll(\".geometry, image\")\n",
       "        .forEach(function (element, i) {\n",
       "            element.transform(t);\n",
       "        });\n",
       "\n",
       "    bounds = root.plotbounds();\n",
       "\n",
       "    if (yscalable) {\n",
       "        var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale);\n",
       "        root.selectAll(\".xfixed\")\n",
       "            .forEach(function (element, i) {\n",
       "                element.transform(xfixed_t);\n",
       "            });\n",
       "\n",
       "        root.select(\".ylabels\")\n",
       "            .transform(xfixed_t)\n",
       "            .selectAll(\"text\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var cx = element.asPX(\"x\"),\n",
       "                        cy = element.asPX(\"y\");\n",
       "                    var st = element.data(\"static_transform\");\n",
       "                    unscale_t = new Snap.Matrix();\n",
       "                    unscale_t.scale(1, 1/scale, cx, cy).add(st);\n",
       "                    element.transform(unscale_t);\n",
       "\n",
       "                    var y = cy * scale + ty;\n",
       "                    element.attr(\"visibility\",\n",
       "                        bounds.y0 <= y && y <= bounds.y1 ? \"visible\" : \"hidden\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    if (xscalable) {\n",
       "        var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);\n",
       "        var xtrans = new Snap.Matrix().translate(tx, 0);\n",
       "        root.selectAll(\".yfixed\")\n",
       "            .forEach(function (element, i) {\n",
       "                element.transform(yfixed_t);\n",
       "            });\n",
       "\n",
       "        root.select(\".xlabels\")\n",
       "            .transform(yfixed_t)\n",
       "            .selectAll(\"text\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var cx = element.asPX(\"x\"),\n",
       "                        cy = element.asPX(\"y\");\n",
       "                    var st = element.data(\"static_transform\");\n",
       "                    unscale_t = new Snap.Matrix();\n",
       "                    unscale_t.scale(1/scale, 1, cx, cy).add(st);\n",
       "\n",
       "                    element.transform(unscale_t);\n",
       "\n",
       "                    var x = cx * scale + tx;\n",
       "                    element.attr(\"visibility\",\n",
       "                        bounds.x0 <= x && x <= bounds.x1 ? \"visible\" : \"hidden\");\n",
       "                    }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    // we must unscale anything that is scale invariance: widths, raiduses, etc.\n",
       "    var size_attribs = [\"font-size\"];\n",
       "    var unscaled_selection = \".geometry, .geometry *\";\n",
       "    if (xscalable) {\n",
       "        size_attribs.push(\"rx\");\n",
       "        unscaled_selection += \", .xgridlines\";\n",
       "    }\n",
       "    if (yscalable) {\n",
       "        size_attribs.push(\"ry\");\n",
       "        unscaled_selection += \", .ygridlines\";\n",
       "    }\n",
       "\n",
       "    root.selectAll(unscaled_selection)\n",
       "        .forEach(function (element, i) {\n",
       "            // circle need special help\n",
       "            if (element.node.nodeName == \"circle\") {\n",
       "                var cx = element.attribute(\"cx\"),\n",
       "                    cy = element.attribute(\"cy\");\n",
       "                unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n",
       "                                                        cx, cy);\n",
       "                element.transform(unscale_t);\n",
       "                return;\n",
       "            }\n",
       "\n",
       "            for (i in size_attribs) {\n",
       "                var key = size_attribs[i];\n",
       "                var val = parseFloat(element.attribute(key));\n",
       "                if (val !== undefined && val != 0 && !isNaN(val)) {\n",
       "                    element.attribute(key, val * old_scale / scale);\n",
       "                }\n",
       "            }\n",
       "        });\n",
       "};\n",
       "\n",
       "\n",
       "// Find the most appropriate tick scale and update label visibility.\n",
       "var update_tickscale = function(root, scale, axis) {\n",
       "    if (!root.hasClass(axis + \"scalable\")) return;\n",
       "\n",
       "    var tickscales = root.data(axis + \"tickscales\");\n",
       "    var best_tickscale = 1.0;\n",
       "    var best_tickscale_dist = Infinity;\n",
       "    for (tickscale in tickscales) {\n",
       "        var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n",
       "        if (dist < best_tickscale_dist) {\n",
       "            best_tickscale_dist = dist;\n",
       "            best_tickscale = tickscale;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    if (best_tickscale != root.data(axis + \"tickscale\")) {\n",
       "        root.data(axis + \"tickscale\", best_tickscale);\n",
       "        var mark_inscale_gridlines = function (element, i) {\n",
       "            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
       "            element.attribute(\"gadfly:inscale\", inscale);\n",
       "            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
       "        };\n",
       "\n",
       "        var mark_inscale_labels = function (element, i) {\n",
       "            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
       "            element.attribute(\"gadfly:inscale\", inscale);\n",
       "            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
       "        };\n",
       "\n",
       "        root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n",
       "        root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "var set_plot_pan_zoom = function(root, tx, ty, scale) {\n",
       "    var old_scale = root.data(\"scale\");\n",
       "    var bounds = root.plotbounds();\n",
       "\n",
       "    var width = bounds.x1 - bounds.x0,\n",
       "        height = bounds.y1 - bounds.y0;\n",
       "\n",
       "    // compute the viewport derived from tx, ty, and scale\n",
       "    var x_min = -width * scale - (scale * width - width),\n",
       "        x_max = width * scale,\n",
       "        y_min = -height * scale - (scale * height - height),\n",
       "        y_max = height * scale;\n",
       "\n",
       "    var x0 = bounds.x0 - scale * bounds.x0,\n",
       "        y0 = bounds.y0 - scale * bounds.y0;\n",
       "\n",
       "    var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n",
       "        ty = Math.max(Math.min(ty - y0, y_max), y_min);\n",
       "\n",
       "    tx += x0;\n",
       "    ty += y0;\n",
       "\n",
       "    // when the scale change, we may need to alter which set of\n",
       "    // ticks is being displayed\n",
       "    if (scale != old_scale) {\n",
       "        update_tickscale(root, scale, \"x\");\n",
       "        update_tickscale(root, scale, \"y\");\n",
       "    }\n",
       "\n",
       "    set_geometry_transform(root, tx, ty, scale);\n",
       "\n",
       "    root.data(\"scale\", scale);\n",
       "    root.data(\"tx\", tx);\n",
       "    root.data(\"ty\", ty);\n",
       "};\n",
       "\n",
       "\n",
       "var scale_centered_translation = function(root, scale) {\n",
       "    var bounds = root.plotbounds();\n",
       "\n",
       "    var width = bounds.x1 - bounds.x0,\n",
       "        height = bounds.y1 - bounds.y0;\n",
       "\n",
       "    var tx0 = root.data(\"tx\"),\n",
       "        ty0 = root.data(\"ty\");\n",
       "\n",
       "    var scale0 = root.data(\"scale\");\n",
       "\n",
       "    // how off from center the current view is\n",
       "    var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n",
       "        yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n",
       "\n",
       "    // rescale offsets\n",
       "    xoff = xoff * scale / scale0;\n",
       "    yoff = yoff * scale / scale0;\n",
       "\n",
       "    // adjust for the panel position being scaled\n",
       "    var x_edge_adjust = bounds.x0 * (1 - scale),\n",
       "        y_edge_adjust = bounds.y0 * (1 - scale);\n",
       "\n",
       "    return {\n",
       "        x: xoff + x_edge_adjust + (width - width * scale) / 2,\n",
       "        y: yoff + y_edge_adjust + (height - height * scale) / 2\n",
       "    };\n",
       "};\n",
       "\n",
       "\n",
       "// Initialize data for panning zooming if it isn't already.\n",
       "var init_pan_zoom = function(root) {\n",
       "    if (root.data(\"zoompan-ready\")) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    // The non-scaling-stroke trick. Rather than try to correct for the\n",
       "    // stroke-width when zooming, we force it to a fixed value.\n",
       "    var px_per_mm = root.node.getCTM().a;\n",
       "\n",
       "    // Drag events report deltas in pixels, which we'd like to convert to\n",
       "    // millimeters.\n",
       "    root.data(\"px_per_mm\", px_per_mm);\n",
       "\n",
       "    root.selectAll(\"path\")\n",
       "        .forEach(function (element, i) {\n",
       "        sw = element.asPX(\"stroke-width\") * px_per_mm;\n",
       "        if (sw > 0) {\n",
       "            element.attribute(\"stroke-width\", sw);\n",
       "            element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n",
       "        }\n",
       "    });\n",
       "\n",
       "    // Store ticks labels original tranformation\n",
       "    root.selectAll(\".xlabels > text, .ylabels > text\")\n",
       "        .forEach(function (element, i) {\n",
       "            var lm = element.transform().localMatrix;\n",
       "            element.data(\"static_transform\",\n",
       "                new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n",
       "        });\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\");\n",
       "    var ygridlines = root.select(\".ygridlines\");\n",
       "    var xlabels = root.select(\".xlabels\");\n",
       "    var ylabels = root.select(\".ylabels\");\n",
       "\n",
       "    if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n",
       "    if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n",
       "    if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n",
       "    if (root.data(\"xtickscales\") === undefined) {\n",
       "\n",
       "        // index all the tick scales that are listed\n",
       "        var xtickscales = {};\n",
       "        var ytickscales = {};\n",
       "        var add_x_tick_scales = function (element, i) {\n",
       "            xtickscales[element.attribute(\"gadfly:scale\")] = true;\n",
       "        };\n",
       "        var add_y_tick_scales = function (element, i) {\n",
       "            ytickscales[element.attribute(\"gadfly:scale\")] = true;\n",
       "        };\n",
       "\n",
       "        if (xgridlines) xgridlines.selectAll(\"path\").forEach(add_x_tick_scales);\n",
       "        if (ygridlines) ygridlines.selectAll(\"path\").forEach(add_y_tick_scales);\n",
       "        if (xlabels) xlabels.selectAll(\"text\").forEach(add_x_tick_scales);\n",
       "        if (ylabels) ylabels.selectAll(\"text\").forEach(add_y_tick_scales);\n",
       "\n",
       "        root.data(\"xtickscales\", xtickscales);\n",
       "        root.data(\"ytickscales\", ytickscales);\n",
       "        root.data(\"xtickscale\", 1.0);\n",
       "    }\n",
       "\n",
       "    var min_scale = 1.0, max_scale = 1.0;\n",
       "    for (scale in xtickscales) {\n",
       "        min_scale = Math.min(min_scale, scale);\n",
       "        max_scale = Math.max(max_scale, scale);\n",
       "    }\n",
       "    for (scale in ytickscales) {\n",
       "        min_scale = Math.min(min_scale, scale);\n",
       "        max_scale = Math.max(max_scale, scale);\n",
       "    }\n",
       "    root.data(\"min_scale\", min_scale);\n",
       "    root.data(\"max_scale\", max_scale);\n",
       "\n",
       "    // store the original positions of labels\n",
       "    if (xlabels) {\n",
       "        xlabels.selectAll(\"text\")\n",
       "               .forEach(function (element, i) {\n",
       "                   element.data(\"x\", element.asPX(\"x\"));\n",
       "               });\n",
       "    }\n",
       "\n",
       "    if (ylabels) {\n",
       "        ylabels.selectAll(\"text\")\n",
       "               .forEach(function (element, i) {\n",
       "                   element.data(\"y\", element.asPX(\"y\"));\n",
       "               });\n",
       "    }\n",
       "\n",
       "    // mark grid lines and ticks as in or out of scale.\n",
       "    var mark_inscale = function (element, i) {\n",
       "        element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n",
       "    };\n",
       "\n",
       "    if (xgridlines) xgridlines.selectAll(\"path\").forEach(mark_inscale);\n",
       "    if (ygridlines) ygridlines.selectAll(\"path\").forEach(mark_inscale);\n",
       "    if (xlabels) xlabels.selectAll(\"text\").forEach(mark_inscale);\n",
       "    if (ylabels) ylabels.selectAll(\"text\").forEach(mark_inscale);\n",
       "\n",
       "    // figure out the upper ond lower bounds on panning using the maximum\n",
       "    // and minum grid lines\n",
       "    var bounds = root.plotbounds();\n",
       "    var pan_bounds = {\n",
       "        x0: 0.0,\n",
       "        y0: 0.0,\n",
       "        x1: 0.0,\n",
       "        y1: 0.0\n",
       "    };\n",
       "\n",
       "    if (xgridlines) {\n",
       "        xgridlines\n",
       "            .selectAll(\"path\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var bbox = element.node.getBBox();\n",
       "                    if (bounds.x1 - bbox.x < pan_bounds.x0) {\n",
       "                        pan_bounds.x0 = bounds.x1 - bbox.x;\n",
       "                    }\n",
       "                    if (bounds.x0 - bbox.x > pan_bounds.x1) {\n",
       "                        pan_bounds.x1 = bounds.x0 - bbox.x;\n",
       "                    }\n",
       "                    element.attr(\"visibility\", \"visible\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    if (ygridlines) {\n",
       "        ygridlines\n",
       "            .selectAll(\"path\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var bbox = element.node.getBBox();\n",
       "                    if (bounds.y1 - bbox.y < pan_bounds.y0) {\n",
       "                        pan_bounds.y0 = bounds.y1 - bbox.y;\n",
       "                    }\n",
       "                    if (bounds.y0 - bbox.y > pan_bounds.y1) {\n",
       "                        pan_bounds.y1 = bounds.y0 - bbox.y;\n",
       "                    }\n",
       "                    element.attr(\"visibility\", \"visible\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    // nudge these values a little\n",
       "    pan_bounds.x0 -= 5;\n",
       "    pan_bounds.x1 += 5;\n",
       "    pan_bounds.y0 -= 5;\n",
       "    pan_bounds.y1 += 5;\n",
       "    root.data(\"pan_bounds\", pan_bounds);\n",
       "\n",
       "    root.data(\"zoompan-ready\", true)\n",
       "};\n",
       "\n",
       "\n",
       "// drag actions, i.e. zooming and panning\n",
       "var pan_action = {\n",
       "    start: function(root, x, y, event) {\n",
       "        root.data(\"dx\", 0);\n",
       "        root.data(\"dy\", 0);\n",
       "        root.data(\"tx0\", root.data(\"tx\"));\n",
       "        root.data(\"ty0\", root.data(\"ty\"));\n",
       "    },\n",
       "    update: function(root, dx, dy, x, y, event) {\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\n",
       "        dx /= px_per_mm;\n",
       "        dy /= px_per_mm;\n",
       "\n",
       "        var tx0 = root.data(\"tx\"),\n",
       "            ty0 = root.data(\"ty\");\n",
       "\n",
       "        var dx0 = root.data(\"dx\"),\n",
       "            dy0 = root.data(\"dy\");\n",
       "\n",
       "        root.data(\"dx\", dx);\n",
       "        root.data(\"dy\", dy);\n",
       "\n",
       "        dx = dx - dx0;\n",
       "        dy = dy - dy0;\n",
       "\n",
       "        var tx = tx0 + dx,\n",
       "            ty = ty0 + dy;\n",
       "\n",
       "        set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n",
       "    },\n",
       "    end: function(root, event) {\n",
       "\n",
       "    },\n",
       "    cancel: function(root) {\n",
       "        set_plot_pan_zoom(root, root.data(\"tx0\"), root.data(\"ty0\"), root.data(\"scale\"));\n",
       "    }\n",
       "};\n",
       "\n",
       "var zoom_box;\n",
       "var zoom_action = {\n",
       "    start: function(root, x, y, event) {\n",
       "        var bounds = root.plotbounds();\n",
       "        var width = bounds.x1 - bounds.x0,\n",
       "            height = bounds.y1 - bounds.y0;\n",
       "        var ratio = width / height;\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\n",
       "            yscalable = root.hasClass(\"yscalable\");\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\n",
       "        x = xscalable ? x / px_per_mm : bounds.x0;\n",
       "        y = yscalable ? y / px_per_mm : bounds.y0;\n",
       "        var w = xscalable ? 0 : width;\n",
       "        var h = yscalable ? 0 : height;\n",
       "        zoom_box = root.rect(x, y, w, h).attr({\n",
       "            \"fill\": \"#000\",\n",
       "            \"opacity\": 0.25\n",
       "        });\n",
       "        zoom_box.data(\"ratio\", ratio);\n",
       "    },\n",
       "    update: function(root, dx, dy, x, y, event) {\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\n",
       "            yscalable = root.hasClass(\"yscalable\");\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\n",
       "        var bounds = root.plotbounds();\n",
       "        if (yscalable) {\n",
       "            y /= px_per_mm;\n",
       "            y = Math.max(bounds.y0, y);\n",
       "            y = Math.min(bounds.y1, y);\n",
       "        } else {\n",
       "            y = bounds.y1;\n",
       "        }\n",
       "        if (xscalable) {\n",
       "            x /= px_per_mm;\n",
       "            x = Math.max(bounds.x0, x);\n",
       "            x = Math.min(bounds.x1, x);\n",
       "        } else {\n",
       "            x = bounds.x1;\n",
       "        }\n",
       "\n",
       "        dx = x - zoom_box.attr(\"x\");\n",
       "        dy = y - zoom_box.attr(\"y\");\n",
       "        if (xscalable && yscalable) {\n",
       "            var ratio = zoom_box.data(\"ratio\");\n",
       "            var width = Math.min(Math.abs(dx), ratio * Math.abs(dy));\n",
       "            var height = Math.min(Math.abs(dy), Math.abs(dx) / ratio);\n",
       "            dx = width * dx / Math.abs(dx);\n",
       "            dy = height * dy / Math.abs(dy);\n",
       "        }\n",
       "        var xoffset = 0,\n",
       "            yoffset = 0;\n",
       "        if (dx < 0) {\n",
       "            xoffset = dx;\n",
       "            dx = -1 * dx;\n",
       "        }\n",
       "        if (dy < 0) {\n",
       "            yoffset = dy;\n",
       "            dy = -1 * dy;\n",
       "        }\n",
       "        if (isNaN(dy)) {\n",
       "            dy = 0.0;\n",
       "        }\n",
       "        if (isNaN(dx)) {\n",
       "            dx = 0.0;\n",
       "        }\n",
       "        zoom_box.transform(\"T\" + xoffset + \",\" + yoffset);\n",
       "        zoom_box.attr(\"width\", dx);\n",
       "        zoom_box.attr(\"height\", dy);\n",
       "    },\n",
       "    end: function(root, event) {\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\n",
       "            yscalable = root.hasClass(\"yscalable\");\n",
       "        var zoom_bounds = zoom_box.getBBox();\n",
       "        if (zoom_bounds.width * zoom_bounds.height <= 0) {\n",
       "            return;\n",
       "        }\n",
       "        var plot_bounds = root.plotbounds();\n",
       "        var zoom_factor = 1.0;\n",
       "        if (yscalable) {\n",
       "            zoom_factor = (plot_bounds.y1 - plot_bounds.y0) / zoom_bounds.height;\n",
       "        } else {\n",
       "            zoom_factor = (plot_bounds.x1 - plot_bounds.x0) / zoom_bounds.width;\n",
       "        }\n",
       "        var tx = (root.data(\"tx\") - zoom_bounds.x) * zoom_factor + plot_bounds.x0,\n",
       "            ty = (root.data(\"ty\") - zoom_bounds.y) * zoom_factor + plot_bounds.y0;\n",
       "        set_plot_pan_zoom(root, tx, ty, root.data(\"scale\") * zoom_factor);\n",
       "        zoom_box.remove();\n",
       "    },\n",
       "    cancel: function(root) {\n",
       "        zoom_box.remove();\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onstart = function(x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var scalable = root.hasClass(\"xscalable\") || root.hasClass(\"yscalable\");\n",
       "    var zoomable = !event.altKey && !event.ctrlKey && event.shiftKey && scalable;\n",
       "    var panable = !event.altKey && !event.ctrlKey && !event.shiftKey && scalable;\n",
       "    var drag_action = zoomable ? zoom_action :\n",
       "                      panable  ? pan_action :\n",
       "                                 undefined;\n",
       "    root.data(\"drag_action\", drag_action);\n",
       "    if (drag_action) {\n",
       "        var cancel_drag_action = function(event) {\n",
       "            if (event.which == 27) { // esc key\n",
       "                drag_action.cancel(root);\n",
       "                root.data(\"drag_action\", undefined);\n",
       "            }\n",
       "        };\n",
       "        window.addEventListener(\"keyup\", cancel_drag_action);\n",
       "        root.data(\"cancel_drag_action\", cancel_drag_action);\n",
       "        drag_action.start(root, x, y, event);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var drag_action = root.data(\"drag_action\");\n",
       "    if (drag_action) {\n",
       "        drag_action.update(root, dx, dy, x, y, event);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onend = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    window.removeEventListener(\"keyup\", root.data(\"cancel_drag_action\"));\n",
       "    root.data(\"cancel_drag_action\", undefined);\n",
       "    var drag_action = root.data(\"drag_action\");\n",
       "    if (drag_action) {\n",
       "        drag_action.end(root, event);\n",
       "    }\n",
       "    root.data(\"drag_action\", undefined);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_scroll = function(event) {\n",
       "    if (event.shiftKey) {\n",
       "        increase_zoom_by_position(this.plotroot(), 0.001 * event.wheelDelta);\n",
       "        event.preventDefault();\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_button_mouseover = function(event) {\n",
       "    this.select(\".button_logo\")\n",
       "         .animate({fill: this.data(\"mouseover_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_button_mouseout = function(event) {\n",
       "     this.select(\".button_logo\")\n",
       "         .animate({fill: this.data(\"mouseout_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_zoomout_click = function(event) {\n",
       "    increase_zoom_by_position(this.plotroot(), -0.1, true);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_zoomin_click = function(event) {\n",
       "    increase_zoom_by_position(this.plotroot(), 0.1, true);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_track_click = function(event) {\n",
       "    // TODO\n",
       "};\n",
       "\n",
       "\n",
       "// Map slider position x to scale y using the function y = a*exp(b*x)+c.\n",
       "// The constants a, b, and c are solved using the constraint that the function\n",
       "// should go through the points (0; min_scale), (0.5; 1), and (1; max_scale).\n",
       "var scale_from_slider_position = function(position, min_scale, max_scale) {\n",
       "    var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
       "        b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
       "        c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
       "    return a * Math.exp(b * position) + c;\n",
       "}\n",
       "\n",
       "// inverse of scale_from_slider_position\n",
       "var slider_position_from_scale = function(scale, min_scale, max_scale) {\n",
       "    var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
       "        b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
       "        c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
       "    return 1 / b * Math.log((scale - c) / a);\n",
       "}\n",
       "\n",
       "var increase_zoom_by_position = function(root, delta_position, animate) {\n",
       "    var scale = root.data(\"scale\"),\n",
       "        min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\");\n",
       "    var position = slider_position_from_scale(scale, min_scale, max_scale);\n",
       "    position += delta_position;\n",
       "    scale = scale_from_slider_position(position, min_scale, max_scale);\n",
       "    set_zoom(root, scale, animate);\n",
       "}\n",
       "\n",
       "var set_zoom = function(root, scale, animate) {\n",
       "    var min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\"),\n",
       "        old_scale = root.data(\"scale\");\n",
       "    var new_scale = Math.max(min_scale, Math.min(scale, max_scale));\n",
       "    if (animate) {\n",
       "        Snap.animate(\n",
       "            old_scale,\n",
       "            new_scale,\n",
       "            function (new_scale) {\n",
       "                update_plot_scale(root, new_scale);\n",
       "            },\n",
       "            200);\n",
       "    } else {\n",
       "        update_plot_scale(root, new_scale);\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "var update_plot_scale = function(root, new_scale) {\n",
       "    var trans = scale_centered_translation(root, new_scale);\n",
       "    set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n",
       "\n",
       "    root.selectAll(\".zoomslider_thumb\")\n",
       "        .forEach(function (element, i) {\n",
       "            var min_pos = element.data(\"min_pos\"),\n",
       "                max_pos = element.data(\"max_pos\"),\n",
       "                min_scale = root.data(\"min_scale\"),\n",
       "                max_scale = root.data(\"max_scale\");\n",
       "            var xmid = (min_pos + max_pos) / 2;\n",
       "            var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n",
       "            element.transform(new Snap.Matrix().translate(\n",
       "                Math.max(min_pos, Math.min(\n",
       "                         max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n",
       "    });\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var min_pos = this.data(\"min_pos\"),\n",
       "        max_pos = this.data(\"max_pos\"),\n",
       "        min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\"),\n",
       "        old_scale = root.data(\"old_scale\");\n",
       "\n",
       "    var px_per_mm = root.data(\"px_per_mm\");\n",
       "    dx /= px_per_mm;\n",
       "    dy /= px_per_mm;\n",
       "\n",
       "    var xmid = (min_pos + max_pos) / 2;\n",
       "    var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n",
       "                   dx / (max_pos - min_pos);\n",
       "\n",
       "    // compute the new scale\n",
       "    var new_scale = scale_from_slider_position(xpos, min_scale, max_scale);\n",
       "    new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n",
       "\n",
       "    update_plot_scale(root, new_scale);\n",
       "    event.stopPropagation();\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragstart = function(x, y, event) {\n",
       "    this.animate({fill: this.data(\"mouseover_color\")}, 100);\n",
       "    var root = this.plotroot();\n",
       "\n",
       "    // keep track of what the scale was when we started dragging\n",
       "    root.data(\"old_scale\", root.data(\"scale\"));\n",
       "    event.stopPropagation();\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragend = function(event) {\n",
       "    this.animate({fill: this.data(\"mouseout_color\")}, 100);\n",
       "    event.stopPropagation();\n",
       "};\n",
       "\n",
       "\n",
       "var toggle_color_class = function(root, color_class, ison) {\n",
       "    var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n",
       "    var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n",
       "    if (ison) {\n",
       "        guides.animate({opacity: 0.5}, 250);\n",
       "        geoms.animate({opacity: 0.0}, 250);\n",
       "    } else {\n",
       "        guides.animate({opacity: 1.0}, 250);\n",
       "        geoms.animate({opacity: 1.0}, 250);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.colorkey_swatch_click = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    var color_class = this.data(\"color_class\");\n",
       "\n",
       "    if (event.shiftKey) {\n",
       "        root.selectAll(\".colorkey text\")\n",
       "            .forEach(function (element) {\n",
       "                var other_color_class = element.data(\"color_class\");\n",
       "                if (other_color_class != color_class) {\n",
       "                    toggle_color_class(root, other_color_class,\n",
       "                                       element.attr(\"opacity\") == 1.0);\n",
       "                }\n",
       "            });\n",
       "    } else {\n",
       "        toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "return Gadfly;\n",
       "\n",
       "}));\n",
       "\n",
       "\n",
       "//@ sourceURL=gadfly.js\n",
       "\n",
       "(function (glob, factory) {\n",
       "    // AMD support\n",
       "      if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n",
       "        require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n",
       "            factory(Snap, Gadfly);\n",
       "        });\n",
       "      } else {\n",
       "          factory(glob.Snap, glob.Gadfly);\n",
       "      }\n",
       "})(window, function (Snap, Gadfly) {\n",
       "    var fig = Snap(\"#img-e72e2ec3\");\n",
       "fig.select(\"#img-e72e2ec3-10\")\n",
       "   .init_gadfly();\n",
       "fig.select(\"#img-e72e2ec3-12\")\n",
       "   .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#img-e72e2ec3-12\")\n",
       "   .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#img-e72e2ec3-13\")\n",
       "   .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#img-e72e2ec3-13\")\n",
       "   .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#img-e72e2ec3-17\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#img-e72e2ec3-17\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#img-e72e2ec3-17\")\n",
       "   .click(Gadfly.zoomslider_zoomin_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "fig.select(\"#img-e72e2ec3-19\")\n",
       "   .data(\"max_pos\", 111.01)\n",
       ";\n",
       "fig.select(\"#img-e72e2ec3-19\")\n",
       "   .data(\"min_pos\", 94.01)\n",
       ";\n",
       "fig.select(\"#img-e72e2ec3-19\")\n",
       "   .click(Gadfly.zoomslider_track_click);\n",
       "fig.select(\"#img-e72e2ec3-20\")\n",
       "   .data(\"max_pos\", 111.01)\n",
       ";\n",
       "fig.select(\"#img-e72e2ec3-20\")\n",
       "   .data(\"min_pos\", 94.01)\n",
       ";\n",
       "fig.select(\"#img-e72e2ec3-20\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#img-e72e2ec3-20\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#img-e72e2ec3-20\")\n",
       "   .drag(Gadfly.zoomslider_thumb_dragmove,\n",
       "     Gadfly.zoomslider_thumb_dragstart,\n",
       "     Gadfly.zoomslider_thumb_dragend)\n",
       ";\n",
       "fig.select(\"#img-e72e2ec3-21\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#img-e72e2ec3-21\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#img-e72e2ec3-21\")\n",
       "   .click(Gadfly.zoomslider_zoomout_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "    });\n",
       "]]> </script>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "Plot(...)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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       "    // AMD support\n",
       "    if (typeof define === \"function\" && define.amd) {\n",
       "        // Define as an anonymous module\n",
       "        define(\"Gadfly\", [\"Snap.svg\"], function (Snap) {\n",
       "            return factory(Snap);\n",
       "        });\n",
       "    } else {\n",
       "        // Browser globals (glob is window)\n",
       "        // Snap adds itself to window\n",
       "        glob.Gadfly = factory(glob.Snap);\n",
       "    }\n",
       "}(this, function (Snap) {\n",
       "\n",
       "var Gadfly = {};\n",
       "\n",
       "// Get an x/y coordinate value in pixels\n",
       "var xPX = function(fig, x) {\n",
       "    var client_box = fig.node.getBoundingClientRect();\n",
       "    return x * fig.node.viewBox.baseVal.width / client_box.width;\n",
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       "\n",
       "var yPX = function(fig, y) {\n",
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       "    return y * fig.node.viewBox.baseVal.height / client_box.height;\n",
       "};\n",
       "\n",
       "\n",
       "Snap.plugin(function (Snap, Element, Paper, global) {\n",
       "    // Traverse upwards from a snap element to find and return the first\n",
       "    // note with the \"plotroot\" class.\n",
       "    Element.prototype.plotroot = function () {\n",
       "        var element = this;\n",
       "        while (!element.hasClass(\"plotroot\") && element.parent() != null) {\n",
       "            element = element.parent();\n",
       "        }\n",
       "        return element;\n",
       "    };\n",
       "\n",
       "    Element.prototype.svgroot = function () {\n",
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       "        while (element.node.nodeName != \"svg\" && element.parent() != null) {\n",
       "            element = element.parent();\n",
       "        }\n",
       "        return element;\n",
       "    };\n",
       "\n",
       "    Element.prototype.plotbounds = function () {\n",
       "        var root = this.plotroot()\n",
       "        var bbox = root.select(\".guide.background\").node.getBBox();\n",
       "        return {\n",
       "            x0: bbox.x,\n",
       "            x1: bbox.x + bbox.width,\n",
       "            y0: bbox.y,\n",
       "            y1: bbox.y + bbox.height\n",
       "        };\n",
       "    };\n",
       "\n",
       "    Element.prototype.plotcenter = function () {\n",
       "        var root = this.plotroot()\n",
       "        var bbox = root.select(\".guide.background\").node.getBBox();\n",
       "        return {\n",
       "            x: bbox.x + bbox.width / 2,\n",
       "            y: bbox.y + bbox.height / 2\n",
       "        };\n",
       "    };\n",
       "\n",
       "    // Emulate IE style mouseenter/mouseleave events, since Microsoft always\n",
       "    // does everything right.\n",
       "    // See: http://www.dynamic-tools.net/toolbox/isMouseLeaveOrEnter/\n",
       "    var events = [\"mouseenter\", \"mouseleave\"];\n",
       "\n",
       "    for (i in events) {\n",
       "        (function (event_name) {\n",
       "            var event_name = events[i];\n",
       "            Element.prototype[event_name] = function (fn, scope) {\n",
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       "                    var fn2 = function (event) {\n",
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       "                            return;\n",
       "                        }\n",
       "\n",
       "                        var reltg = event.relatedTarget ? event.relatedTarget :\n",
       "                            event.type == \"mouseout\" ? event.toElement : event.fromElement;\n",
       "                        while (reltg && reltg != this.node) reltg = reltg.parentNode;\n",
       "\n",
       "                        if (reltg != this.node) {\n",
       "                            return fn.apply(this, event);\n",
       "                        }\n",
       "                    };\n",
       "\n",
       "                    if (event_name == \"mouseenter\") {\n",
       "                        this.mouseover(fn2, scope);\n",
       "                    } else {\n",
       "                        this.mouseout(fn2, scope);\n",
       "                    }\n",
       "                }\n",
       "                return this;\n",
       "            };\n",
       "        })(events[i]);\n",
       "    }\n",
       "\n",
       "\n",
       "    Element.prototype.mousewheel = function (fn, scope) {\n",
       "        if (Snap.is(fn, \"function\")) {\n",
       "            var el = this;\n",
       "            var fn2 = function (event) {\n",
       "                fn.apply(el, [event]);\n",
       "            };\n",
       "        }\n",
       "\n",
       "        this.node.addEventListener(\n",
       "            /Firefox/i.test(navigator.userAgent) ? \"DOMMouseScroll\" : \"mousewheel\",\n",
       "            fn2);\n",
       "\n",
       "        return this;\n",
       "    };\n",
       "\n",
       "\n",
       "    // Snap's attr function can be too slow for things like panning/zooming.\n",
       "    // This is a function to directly update element attributes without going\n",
       "    // through eve.\n",
       "    Element.prototype.attribute = function(key, val) {\n",
       "        if (val === undefined) {\n",
       "            return this.node.getAttribute(key);\n",
       "        } else {\n",
       "            this.node.setAttribute(key, val);\n",
       "            return this;\n",
       "        }\n",
       "    };\n",
       "\n",
       "    Element.prototype.init_gadfly = function() {\n",
       "        this.mouseenter(Gadfly.plot_mouseover)\n",
       "            .mouseleave(Gadfly.plot_mouseout)\n",
       "            .dblclick(Gadfly.plot_dblclick)\n",
       "            .mousewheel(Gadfly.guide_background_scroll)\n",
       "            .drag(Gadfly.guide_background_drag_onmove,\n",
       "                  Gadfly.guide_background_drag_onstart,\n",
       "                  Gadfly.guide_background_drag_onend);\n",
       "        this.mouseenter(function (event) {\n",
       "            init_pan_zoom(this.plotroot());\n",
       "        });\n",
       "        return this;\n",
       "    };\n",
       "});\n",
       "\n",
       "\n",
       "// When the plot is moused over, emphasize the grid lines.\n",
       "Gadfly.plot_mouseover = function(event) {\n",
       "    var root = this.plotroot();\n",
       "\n",
       "    var keyboard_zoom = function(event) {\n",
       "        if (event.which == 187) { // plus\n",
       "            increase_zoom_by_position(root, 0.1, true);\n",
       "        } else if (event.which == 189) { // minus\n",
       "            increase_zoom_by_position(root, -0.1, true);\n",
       "        }\n",
       "    };\n",
       "    root.data(\"keyboard_zoom\", keyboard_zoom);\n",
       "    window.addEventListener(\"keyup\", keyboard_zoom);\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\"),\n",
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       "\n",
       "    // emphasize grid lines\n",
       "    var destcolor = root.data(\"focused_xgrid_color\");\n",
       "    xgridlines.attribute(\"stroke-dasharray\", \"none\")\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"focused_ygrid_color\");\n",
       "    ygridlines.attribute(\"stroke-dasharray\", \"none\")\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    // reveal zoom slider\n",
       "    root.select(\".zoomslider\")\n",
       "        .animate({opacity: 1.0}, 250);\n",
       "};\n",
       "\n",
       "// Reset pan and zoom on double click\n",
       "Gadfly.plot_dblclick = function(event) {\n",
       "  set_plot_pan_zoom(this.plotroot(), 0.0, 0.0, 1.0);\n",
       "};\n",
       "\n",
       "// Unemphasize grid lines on mouse out.\n",
       "Gadfly.plot_mouseout = function(event) {\n",
       "    var root = this.plotroot();\n",
       "\n",
       "    window.removeEventListener(\"keyup\", root.data(\"keyboard_zoom\"));\n",
       "    root.data(\"keyboard_zoom\", undefined);\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\"),\n",
       "        ygridlines = root.select(\".ygridlines\");\n",
       "\n",
       "    var destcolor = root.data(\"unfocused_xgrid_color\");\n",
       "\n",
       "    xgridlines.attribute(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"unfocused_ygrid_color\");\n",
       "    ygridlines.attribute(\"stroke-dasharray\", ygridlines.data(\"unfocused_strokedash\"))\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    // hide zoom slider\n",
       "    root.select(\".zoomslider\")\n",
       "        .animate({opacity: 0.0}, 250);\n",
       "};\n",
       "\n",
       "\n",
       "var set_geometry_transform = function(root, tx, ty, scale) {\n",
       "    var xscalable = root.hasClass(\"xscalable\"),\n",
       "        yscalable = root.hasClass(\"yscalable\");\n",
       "\n",
       "    var old_scale = root.data(\"scale\");\n",
       "\n",
       "    var xscale = xscalable ? scale : 1.0,\n",
       "        yscale = yscalable ? scale : 1.0;\n",
       "\n",
       "    tx = xscalable ? tx : 0.0;\n",
       "    ty = yscalable ? ty : 0.0;\n",
       "\n",
       "    var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale);\n",
       "\n",
       "    root.selectAll(\".geometry, image\")\n",
       "        .forEach(function (element, i) {\n",
       "            element.transform(t);\n",
       "        });\n",
       "\n",
       "    bounds = root.plotbounds();\n",
       "\n",
       "    if (yscalable) {\n",
       "        var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale);\n",
       "        root.selectAll(\".xfixed\")\n",
       "            .forEach(function (element, i) {\n",
       "                element.transform(xfixed_t);\n",
       "            });\n",
       "\n",
       "        root.select(\".ylabels\")\n",
       "            .transform(xfixed_t)\n",
       "            .selectAll(\"text\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var cx = element.asPX(\"x\"),\n",
       "                        cy = element.asPX(\"y\");\n",
       "                    var st = element.data(\"static_transform\");\n",
       "                    unscale_t = new Snap.Matrix();\n",
       "                    unscale_t.scale(1, 1/scale, cx, cy).add(st);\n",
       "                    element.transform(unscale_t);\n",
       "\n",
       "                    var y = cy * scale + ty;\n",
       "                    element.attr(\"visibility\",\n",
       "                        bounds.y0 <= y && y <= bounds.y1 ? \"visible\" : \"hidden\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    if (xscalable) {\n",
       "        var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);\n",
       "        var xtrans = new Snap.Matrix().translate(tx, 0);\n",
       "        root.selectAll(\".yfixed\")\n",
       "            .forEach(function (element, i) {\n",
       "                element.transform(yfixed_t);\n",
       "            });\n",
       "\n",
       "        root.select(\".xlabels\")\n",
       "            .transform(yfixed_t)\n",
       "            .selectAll(\"text\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var cx = element.asPX(\"x\"),\n",
       "                        cy = element.asPX(\"y\");\n",
       "                    var st = element.data(\"static_transform\");\n",
       "                    unscale_t = new Snap.Matrix();\n",
       "                    unscale_t.scale(1/scale, 1, cx, cy).add(st);\n",
       "\n",
       "                    element.transform(unscale_t);\n",
       "\n",
       "                    var x = cx * scale + tx;\n",
       "                    element.attr(\"visibility\",\n",
       "                        bounds.x0 <= x && x <= bounds.x1 ? \"visible\" : \"hidden\");\n",
       "                    }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    // we must unscale anything that is scale invariance: widths, raiduses, etc.\n",
       "    var size_attribs = [\"font-size\"];\n",
       "    var unscaled_selection = \".geometry, .geometry *\";\n",
       "    if (xscalable) {\n",
       "        size_attribs.push(\"rx\");\n",
       "        unscaled_selection += \", .xgridlines\";\n",
       "    }\n",
       "    if (yscalable) {\n",
       "        size_attribs.push(\"ry\");\n",
       "        unscaled_selection += \", .ygridlines\";\n",
       "    }\n",
       "\n",
       "    root.selectAll(unscaled_selection)\n",
       "        .forEach(function (element, i) {\n",
       "            // circle need special help\n",
       "            if (element.node.nodeName == \"circle\") {\n",
       "                var cx = element.attribute(\"cx\"),\n",
       "                    cy = element.attribute(\"cy\");\n",
       "                unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n",
       "                                                        cx, cy);\n",
       "                element.transform(unscale_t);\n",
       "                return;\n",
       "            }\n",
       "\n",
       "            for (i in size_attribs) {\n",
       "                var key = size_attribs[i];\n",
       "                var val = parseFloat(element.attribute(key));\n",
       "                if (val !== undefined && val != 0 && !isNaN(val)) {\n",
       "                    element.attribute(key, val * old_scale / scale);\n",
       "                }\n",
       "            }\n",
       "        });\n",
       "};\n",
       "\n",
       "\n",
       "// Find the most appropriate tick scale and update label visibility.\n",
       "var update_tickscale = function(root, scale, axis) {\n",
       "    if (!root.hasClass(axis + \"scalable\")) return;\n",
       "\n",
       "    var tickscales = root.data(axis + \"tickscales\");\n",
       "    var best_tickscale = 1.0;\n",
       "    var best_tickscale_dist = Infinity;\n",
       "    for (tickscale in tickscales) {\n",
       "        var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n",
       "        if (dist < best_tickscale_dist) {\n",
       "            best_tickscale_dist = dist;\n",
       "            best_tickscale = tickscale;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    if (best_tickscale != root.data(axis + \"tickscale\")) {\n",
       "        root.data(axis + \"tickscale\", best_tickscale);\n",
       "        var mark_inscale_gridlines = function (element, i) {\n",
       "            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
       "            element.attribute(\"gadfly:inscale\", inscale);\n",
       "            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
       "        };\n",
       "\n",
       "        var mark_inscale_labels = function (element, i) {\n",
       "            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
       "            element.attribute(\"gadfly:inscale\", inscale);\n",
       "            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
       "        };\n",
       "\n",
       "        root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n",
       "        root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "var set_plot_pan_zoom = function(root, tx, ty, scale) {\n",
       "    var old_scale = root.data(\"scale\");\n",
       "    var bounds = root.plotbounds();\n",
       "\n",
       "    var width = bounds.x1 - bounds.x0,\n",
       "        height = bounds.y1 - bounds.y0;\n",
       "\n",
       "    // compute the viewport derived from tx, ty, and scale\n",
       "    var x_min = -width * scale - (scale * width - width),\n",
       "        x_max = width * scale,\n",
       "        y_min = -height * scale - (scale * height - height),\n",
       "        y_max = height * scale;\n",
       "\n",
       "    var x0 = bounds.x0 - scale * bounds.x0,\n",
       "        y0 = bounds.y0 - scale * bounds.y0;\n",
       "\n",
       "    var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n",
       "        ty = Math.max(Math.min(ty - y0, y_max), y_min);\n",
       "\n",
       "    tx += x0;\n",
       "    ty += y0;\n",
       "\n",
       "    // when the scale change, we may need to alter which set of\n",
       "    // ticks is being displayed\n",
       "    if (scale != old_scale) {\n",
       "        update_tickscale(root, scale, \"x\");\n",
       "        update_tickscale(root, scale, \"y\");\n",
       "    }\n",
       "\n",
       "    set_geometry_transform(root, tx, ty, scale);\n",
       "\n",
       "    root.data(\"scale\", scale);\n",
       "    root.data(\"tx\", tx);\n",
       "    root.data(\"ty\", ty);\n",
       "};\n",
       "\n",
       "\n",
       "var scale_centered_translation = function(root, scale) {\n",
       "    var bounds = root.plotbounds();\n",
       "\n",
       "    var width = bounds.x1 - bounds.x0,\n",
       "        height = bounds.y1 - bounds.y0;\n",
       "\n",
       "    var tx0 = root.data(\"tx\"),\n",
       "        ty0 = root.data(\"ty\");\n",
       "\n",
       "    var scale0 = root.data(\"scale\");\n",
       "\n",
       "    // how off from center the current view is\n",
       "    var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n",
       "        yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n",
       "\n",
       "    // rescale offsets\n",
       "    xoff = xoff * scale / scale0;\n",
       "    yoff = yoff * scale / scale0;\n",
       "\n",
       "    // adjust for the panel position being scaled\n",
       "    var x_edge_adjust = bounds.x0 * (1 - scale),\n",
       "        y_edge_adjust = bounds.y0 * (1 - scale);\n",
       "\n",
       "    return {\n",
       "        x: xoff + x_edge_adjust + (width - width * scale) / 2,\n",
       "        y: yoff + y_edge_adjust + (height - height * scale) / 2\n",
       "    };\n",
       "};\n",
       "\n",
       "\n",
       "// Initialize data for panning zooming if it isn't already.\n",
       "var init_pan_zoom = function(root) {\n",
       "    if (root.data(\"zoompan-ready\")) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    // The non-scaling-stroke trick. Rather than try to correct for the\n",
       "    // stroke-width when zooming, we force it to a fixed value.\n",
       "    var px_per_mm = root.node.getCTM().a;\n",
       "\n",
       "    // Drag events report deltas in pixels, which we'd like to convert to\n",
       "    // millimeters.\n",
       "    root.data(\"px_per_mm\", px_per_mm);\n",
       "\n",
       "    root.selectAll(\"path\")\n",
       "        .forEach(function (element, i) {\n",
       "        sw = element.asPX(\"stroke-width\") * px_per_mm;\n",
       "        if (sw > 0) {\n",
       "            element.attribute(\"stroke-width\", sw);\n",
       "            element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n",
       "        }\n",
       "    });\n",
       "\n",
       "    // Store ticks labels original tranformation\n",
       "    root.selectAll(\".xlabels > text, .ylabels > text\")\n",
       "        .forEach(function (element, i) {\n",
       "            var lm = element.transform().localMatrix;\n",
       "            element.data(\"static_transform\",\n",
       "                new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n",
       "        });\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\");\n",
       "    var ygridlines = root.select(\".ygridlines\");\n",
       "    var xlabels = root.select(\".xlabels\");\n",
       "    var ylabels = root.select(\".ylabels\");\n",
       "\n",
       "    if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n",
       "    if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n",
       "    if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n",
       "    if (root.data(\"xtickscales\") === undefined) {\n",
       "\n",
       "        // index all the tick scales that are listed\n",
       "        var xtickscales = {};\n",
       "        var ytickscales = {};\n",
       "        var add_x_tick_scales = function (element, i) {\n",
       "            xtickscales[element.attribute(\"gadfly:scale\")] = true;\n",
       "        };\n",
       "        var add_y_tick_scales = function (element, i) {\n",
       "            ytickscales[element.attribute(\"gadfly:scale\")] = true;\n",
       "        };\n",
       "\n",
       "        if (xgridlines) xgridlines.selectAll(\"path\").forEach(add_x_tick_scales);\n",
       "        if (ygridlines) ygridlines.selectAll(\"path\").forEach(add_y_tick_scales);\n",
       "        if (xlabels) xlabels.selectAll(\"text\").forEach(add_x_tick_scales);\n",
       "        if (ylabels) ylabels.selectAll(\"text\").forEach(add_y_tick_scales);\n",
       "\n",
       "        root.data(\"xtickscales\", xtickscales);\n",
       "        root.data(\"ytickscales\", ytickscales);\n",
       "        root.data(\"xtickscale\", 1.0);\n",
       "    }\n",
       "\n",
       "    var min_scale = 1.0, max_scale = 1.0;\n",
       "    for (scale in xtickscales) {\n",
       "        min_scale = Math.min(min_scale, scale);\n",
       "        max_scale = Math.max(max_scale, scale);\n",
       "    }\n",
       "    for (scale in ytickscales) {\n",
       "        min_scale = Math.min(min_scale, scale);\n",
       "        max_scale = Math.max(max_scale, scale);\n",
       "    }\n",
       "    root.data(\"min_scale\", min_scale);\n",
       "    root.data(\"max_scale\", max_scale);\n",
       "\n",
       "    // store the original positions of labels\n",
       "    if (xlabels) {\n",
       "        xlabels.selectAll(\"text\")\n",
       "               .forEach(function (element, i) {\n",
       "                   element.data(\"x\", element.asPX(\"x\"));\n",
       "               });\n",
       "    }\n",
       "\n",
       "    if (ylabels) {\n",
       "        ylabels.selectAll(\"text\")\n",
       "               .forEach(function (element, i) {\n",
       "                   element.data(\"y\", element.asPX(\"y\"));\n",
       "               });\n",
       "    }\n",
       "\n",
       "    // mark grid lines and ticks as in or out of scale.\n",
       "    var mark_inscale = function (element, i) {\n",
       "        element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n",
       "    };\n",
       "\n",
       "    if (xgridlines) xgridlines.selectAll(\"path\").forEach(mark_inscale);\n",
       "    if (ygridlines) ygridlines.selectAll(\"path\").forEach(mark_inscale);\n",
       "    if (xlabels) xlabels.selectAll(\"text\").forEach(mark_inscale);\n",
       "    if (ylabels) ylabels.selectAll(\"text\").forEach(mark_inscale);\n",
       "\n",
       "    // figure out the upper ond lower bounds on panning using the maximum\n",
       "    // and minum grid lines\n",
       "    var bounds = root.plotbounds();\n",
       "    var pan_bounds = {\n",
       "        x0: 0.0,\n",
       "        y0: 0.0,\n",
       "        x1: 0.0,\n",
       "        y1: 0.0\n",
       "    };\n",
       "\n",
       "    if (xgridlines) {\n",
       "        xgridlines\n",
       "            .selectAll(\"path\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var bbox = element.node.getBBox();\n",
       "                    if (bounds.x1 - bbox.x < pan_bounds.x0) {\n",
       "                        pan_bounds.x0 = bounds.x1 - bbox.x;\n",
       "                    }\n",
       "                    if (bounds.x0 - bbox.x > pan_bounds.x1) {\n",
       "                        pan_bounds.x1 = bounds.x0 - bbox.x;\n",
       "                    }\n",
       "                    element.attr(\"visibility\", \"visible\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    if (ygridlines) {\n",
       "        ygridlines\n",
       "            .selectAll(\"path\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var bbox = element.node.getBBox();\n",
       "                    if (bounds.y1 - bbox.y < pan_bounds.y0) {\n",
       "                        pan_bounds.y0 = bounds.y1 - bbox.y;\n",
       "                    }\n",
       "                    if (bounds.y0 - bbox.y > pan_bounds.y1) {\n",
       "                        pan_bounds.y1 = bounds.y0 - bbox.y;\n",
       "                    }\n",
       "                    element.attr(\"visibility\", \"visible\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    // nudge these values a little\n",
       "    pan_bounds.x0 -= 5;\n",
       "    pan_bounds.x1 += 5;\n",
       "    pan_bounds.y0 -= 5;\n",
       "    pan_bounds.y1 += 5;\n",
       "    root.data(\"pan_bounds\", pan_bounds);\n",
       "\n",
       "    root.data(\"zoompan-ready\", true)\n",
       "};\n",
       "\n",
       "\n",
       "// drag actions, i.e. zooming and panning\n",
       "var pan_action = {\n",
       "    start: function(root, x, y, event) {\n",
       "        root.data(\"dx\", 0);\n",
       "        root.data(\"dy\", 0);\n",
       "        root.data(\"tx0\", root.data(\"tx\"));\n",
       "        root.data(\"ty0\", root.data(\"ty\"));\n",
       "    },\n",
       "    update: function(root, dx, dy, x, y, event) {\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\n",
       "        dx /= px_per_mm;\n",
       "        dy /= px_per_mm;\n",
       "\n",
       "        var tx0 = root.data(\"tx\"),\n",
       "            ty0 = root.data(\"ty\");\n",
       "\n",
       "        var dx0 = root.data(\"dx\"),\n",
       "            dy0 = root.data(\"dy\");\n",
       "\n",
       "        root.data(\"dx\", dx);\n",
       "        root.data(\"dy\", dy);\n",
       "\n",
       "        dx = dx - dx0;\n",
       "        dy = dy - dy0;\n",
       "\n",
       "        var tx = tx0 + dx,\n",
       "            ty = ty0 + dy;\n",
       "\n",
       "        set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n",
       "    },\n",
       "    end: function(root, event) {\n",
       "\n",
       "    },\n",
       "    cancel: function(root) {\n",
       "        set_plot_pan_zoom(root, root.data(\"tx0\"), root.data(\"ty0\"), root.data(\"scale\"));\n",
       "    }\n",
       "};\n",
       "\n",
       "var zoom_box;\n",
       "var zoom_action = {\n",
       "    start: function(root, x, y, event) {\n",
       "        var bounds = root.plotbounds();\n",
       "        var width = bounds.x1 - bounds.x0,\n",
       "            height = bounds.y1 - bounds.y0;\n",
       "        var ratio = width / height;\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\n",
       "            yscalable = root.hasClass(\"yscalable\");\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\n",
       "        x = xscalable ? x / px_per_mm : bounds.x0;\n",
       "        y = yscalable ? y / px_per_mm : bounds.y0;\n",
       "        var w = xscalable ? 0 : width;\n",
       "        var h = yscalable ? 0 : height;\n",
       "        zoom_box = root.rect(x, y, w, h).attr({\n",
       "            \"fill\": \"#000\",\n",
       "            \"opacity\": 0.25\n",
       "        });\n",
       "        zoom_box.data(\"ratio\", ratio);\n",
       "    },\n",
       "    update: function(root, dx, dy, x, y, event) {\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\n",
       "            yscalable = root.hasClass(\"yscalable\");\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\n",
       "        var bounds = root.plotbounds();\n",
       "        if (yscalable) {\n",
       "            y /= px_per_mm;\n",
       "            y = Math.max(bounds.y0, y);\n",
       "            y = Math.min(bounds.y1, y);\n",
       "        } else {\n",
       "            y = bounds.y1;\n",
       "        }\n",
       "        if (xscalable) {\n",
       "            x /= px_per_mm;\n",
       "            x = Math.max(bounds.x0, x);\n",
       "            x = Math.min(bounds.x1, x);\n",
       "        } else {\n",
       "            x = bounds.x1;\n",
       "        }\n",
       "\n",
       "        dx = x - zoom_box.attr(\"x\");\n",
       "        dy = y - zoom_box.attr(\"y\");\n",
       "        if (xscalable && yscalable) {\n",
       "            var ratio = zoom_box.data(\"ratio\");\n",
       "            var width = Math.min(Math.abs(dx), ratio * Math.abs(dy));\n",
       "            var height = Math.min(Math.abs(dy), Math.abs(dx) / ratio);\n",
       "            dx = width * dx / Math.abs(dx);\n",
       "            dy = height * dy / Math.abs(dy);\n",
       "        }\n",
       "        var xoffset = 0,\n",
       "            yoffset = 0;\n",
       "        if (dx < 0) {\n",
       "            xoffset = dx;\n",
       "            dx = -1 * dx;\n",
       "        }\n",
       "        if (dy < 0) {\n",
       "            yoffset = dy;\n",
       "            dy = -1 * dy;\n",
       "        }\n",
       "        if (isNaN(dy)) {\n",
       "            dy = 0.0;\n",
       "        }\n",
       "        if (isNaN(dx)) {\n",
       "            dx = 0.0;\n",
       "        }\n",
       "        zoom_box.transform(\"T\" + xoffset + \",\" + yoffset);\n",
       "        zoom_box.attr(\"width\", dx);\n",
       "        zoom_box.attr(\"height\", dy);\n",
       "    },\n",
       "    end: function(root, event) {\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\n",
       "            yscalable = root.hasClass(\"yscalable\");\n",
       "        var zoom_bounds = zoom_box.getBBox();\n",
       "        if (zoom_bounds.width * zoom_bounds.height <= 0) {\n",
       "            return;\n",
       "        }\n",
       "        var plot_bounds = root.plotbounds();\n",
       "        var zoom_factor = 1.0;\n",
       "        if (yscalable) {\n",
       "            zoom_factor = (plot_bounds.y1 - plot_bounds.y0) / zoom_bounds.height;\n",
       "        } else {\n",
       "            zoom_factor = (plot_bounds.x1 - plot_bounds.x0) / zoom_bounds.width;\n",
       "        }\n",
       "        var tx = (root.data(\"tx\") - zoom_bounds.x) * zoom_factor + plot_bounds.x0,\n",
       "            ty = (root.data(\"ty\") - zoom_bounds.y) * zoom_factor + plot_bounds.y0;\n",
       "        set_plot_pan_zoom(root, tx, ty, root.data(\"scale\") * zoom_factor);\n",
       "        zoom_box.remove();\n",
       "    },\n",
       "    cancel: function(root) {\n",
       "        zoom_box.remove();\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onstart = function(x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var scalable = root.hasClass(\"xscalable\") || root.hasClass(\"yscalable\");\n",
       "    var zoomable = !event.altKey && !event.ctrlKey && event.shiftKey && scalable;\n",
       "    var panable = !event.altKey && !event.ctrlKey && !event.shiftKey && scalable;\n",
       "    var drag_action = zoomable ? zoom_action :\n",
       "                      panable  ? pan_action :\n",
       "                                 undefined;\n",
       "    root.data(\"drag_action\", drag_action);\n",
       "    if (drag_action) {\n",
       "        var cancel_drag_action = function(event) {\n",
       "            if (event.which == 27) { // esc key\n",
       "                drag_action.cancel(root);\n",
       "                root.data(\"drag_action\", undefined);\n",
       "            }\n",
       "        };\n",
       "        window.addEventListener(\"keyup\", cancel_drag_action);\n",
       "        root.data(\"cancel_drag_action\", cancel_drag_action);\n",
       "        drag_action.start(root, x, y, event);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var drag_action = root.data(\"drag_action\");\n",
       "    if (drag_action) {\n",
       "        drag_action.update(root, dx, dy, x, y, event);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onend = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    window.removeEventListener(\"keyup\", root.data(\"cancel_drag_action\"));\n",
       "    root.data(\"cancel_drag_action\", undefined);\n",
       "    var drag_action = root.data(\"drag_action\");\n",
       "    if (drag_action) {\n",
       "        drag_action.end(root, event);\n",
       "    }\n",
       "    root.data(\"drag_action\", undefined);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_scroll = function(event) {\n",
       "    if (event.shiftKey) {\n",
       "        increase_zoom_by_position(this.plotroot(), 0.001 * event.wheelDelta);\n",
       "        event.preventDefault();\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_button_mouseover = function(event) {\n",
       "    this.select(\".button_logo\")\n",
       "         .animate({fill: this.data(\"mouseover_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_button_mouseout = function(event) {\n",
       "     this.select(\".button_logo\")\n",
       "         .animate({fill: this.data(\"mouseout_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_zoomout_click = function(event) {\n",
       "    increase_zoom_by_position(this.plotroot(), -0.1, true);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_zoomin_click = function(event) {\n",
       "    increase_zoom_by_position(this.plotroot(), 0.1, true);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_track_click = function(event) {\n",
       "    // TODO\n",
       "};\n",
       "\n",
       "\n",
       "// Map slider position x to scale y using the function y = a*exp(b*x)+c.\n",
       "// The constants a, b, and c are solved using the constraint that the function\n",
       "// should go through the points (0; min_scale), (0.5; 1), and (1; max_scale).\n",
       "var scale_from_slider_position = function(position, min_scale, max_scale) {\n",
       "    var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
       "        b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
       "        c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
       "    return a * Math.exp(b * position) + c;\n",
       "}\n",
       "\n",
       "// inverse of scale_from_slider_position\n",
       "var slider_position_from_scale = function(scale, min_scale, max_scale) {\n",
       "    var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
       "        b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
       "        c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
       "    return 1 / b * Math.log((scale - c) / a);\n",
       "}\n",
       "\n",
       "var increase_zoom_by_position = function(root, delta_position, animate) {\n",
       "    var scale = root.data(\"scale\"),\n",
       "        min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\");\n",
       "    var position = slider_position_from_scale(scale, min_scale, max_scale);\n",
       "    position += delta_position;\n",
       "    scale = scale_from_slider_position(position, min_scale, max_scale);\n",
       "    set_zoom(root, scale, animate);\n",
       "}\n",
       "\n",
       "var set_zoom = function(root, scale, animate) {\n",
       "    var min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\"),\n",
       "        old_scale = root.data(\"scale\");\n",
       "    var new_scale = Math.max(min_scale, Math.min(scale, max_scale));\n",
       "    if (animate) {\n",
       "        Snap.animate(\n",
       "            old_scale,\n",
       "            new_scale,\n",
       "            function (new_scale) {\n",
       "                update_plot_scale(root, new_scale);\n",
       "            },\n",
       "            200);\n",
       "    } else {\n",
       "        update_plot_scale(root, new_scale);\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "var update_plot_scale = function(root, new_scale) {\n",
       "    var trans = scale_centered_translation(root, new_scale);\n",
       "    set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n",
       "\n",
       "    root.selectAll(\".zoomslider_thumb\")\n",
       "        .forEach(function (element, i) {\n",
       "            var min_pos = element.data(\"min_pos\"),\n",
       "                max_pos = element.data(\"max_pos\"),\n",
       "                min_scale = root.data(\"min_scale\"),\n",
       "                max_scale = root.data(\"max_scale\");\n",
       "            var xmid = (min_pos + max_pos) / 2;\n",
       "            var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n",
       "            element.transform(new Snap.Matrix().translate(\n",
       "                Math.max(min_pos, Math.min(\n",
       "                         max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n",
       "    });\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var min_pos = this.data(\"min_pos\"),\n",
       "        max_pos = this.data(\"max_pos\"),\n",
       "        min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\"),\n",
       "        old_scale = root.data(\"old_scale\");\n",
       "\n",
       "    var px_per_mm = root.data(\"px_per_mm\");\n",
       "    dx /= px_per_mm;\n",
       "    dy /= px_per_mm;\n",
       "\n",
       "    var xmid = (min_pos + max_pos) / 2;\n",
       "    var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n",
       "                   dx / (max_pos - min_pos);\n",
       "\n",
       "    // compute the new scale\n",
       "    var new_scale = scale_from_slider_position(xpos, min_scale, max_scale);\n",
       "    new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n",
       "\n",
       "    update_plot_scale(root, new_scale);\n",
       "    event.stopPropagation();\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragstart = function(x, y, event) {\n",
       "    this.animate({fill: this.data(\"mouseover_color\")}, 100);\n",
       "    var root = this.plotroot();\n",
       "\n",
       "    // keep track of what the scale was when we started dragging\n",
       "    root.data(\"old_scale\", root.data(\"scale\"));\n",
       "    event.stopPropagation();\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragend = function(event) {\n",
       "    this.animate({fill: this.data(\"mouseout_color\")}, 100);\n",
       "    event.stopPropagation();\n",
       "};\n",
       "\n",
       "\n",
       "var toggle_color_class = function(root, color_class, ison) {\n",
       "    var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n",
       "    var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n",
       "    if (ison) {\n",
       "        guides.animate({opacity: 0.5}, 250);\n",
       "        geoms.animate({opacity: 0.0}, 250);\n",
       "    } else {\n",
       "        guides.animate({opacity: 1.0}, 250);\n",
       "        geoms.animate({opacity: 1.0}, 250);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.colorkey_swatch_click = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    var color_class = this.data(\"color_class\");\n",
       "\n",
       "    if (event.shiftKey) {\n",
       "        root.selectAll(\".colorkey text\")\n",
       "            .forEach(function (element) {\n",
       "                var other_color_class = element.data(\"color_class\");\n",
       "                if (other_color_class != color_class) {\n",
       "                    toggle_color_class(root, other_color_class,\n",
       "                                       element.attr(\"opacity\") == 1.0);\n",
       "                }\n",
       "            });\n",
       "    } else {\n",
       "        toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "return Gadfly;\n",
       "\n",
       "}));\n",
       "\n",
       "\n",
       "//@ sourceURL=gadfly.js\n",
       "\n",
       "(function (glob, factory) {\n",
       "    // AMD support\n",
       "      if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n",
       "        require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n",
       "            factory(Snap, Gadfly);\n",
       "        });\n",
       "      } else {\n",
       "          factory(glob.Snap, glob.Gadfly);\n",
       "      }\n",
       "})(window, function (Snap, Gadfly) {\n",
       "    var fig = Snap(\"#img-7086f8a5\");\n",
       "fig.select(\"#img-7086f8a5-10\")\n",
       "   .init_gadfly();\n",
       "fig.select(\"#img-7086f8a5-12\")\n",
       "   .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#img-7086f8a5-12\")\n",
       "   .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#img-7086f8a5-13\")\n",
       "   .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#img-7086f8a5-13\")\n",
       "   .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#img-7086f8a5-17\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#img-7086f8a5-17\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#img-7086f8a5-17\")\n",
       "   .click(Gadfly.zoomslider_zoomin_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "fig.select(\"#img-7086f8a5-19\")\n",
       "   .data(\"max_pos\", 111.01)\n",
       ";\n",
       "fig.select(\"#img-7086f8a5-19\")\n",
       "   .data(\"min_pos\", 94.01)\n",
       ";\n",
       "fig.select(\"#img-7086f8a5-19\")\n",
       "   .click(Gadfly.zoomslider_track_click);\n",
       "fig.select(\"#img-7086f8a5-20\")\n",
       "   .data(\"max_pos\", 111.01)\n",
       ";\n",
       "fig.select(\"#img-7086f8a5-20\")\n",
       "   .data(\"min_pos\", 94.01)\n",
       ";\n",
       "fig.select(\"#img-7086f8a5-20\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#img-7086f8a5-20\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#img-7086f8a5-20\")\n",
       "   .drag(Gadfly.zoomslider_thumb_dragmove,\n",
       "     Gadfly.zoomslider_thumb_dragstart,\n",
       "     Gadfly.zoomslider_thumb_dragend)\n",
       ";\n",
       "fig.select(\"#img-7086f8a5-21\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#img-7086f8a5-21\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#img-7086f8a5-21\")\n",
       "   .click(Gadfly.zoomslider_zoomout_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "    });\n",
       "]]> </script>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "Plot(...)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = 1:5\n",
    "ys = 1:8\n",
    "g = Float64[x^2 * sin(y) for x in xs, y in ys]\n",
    "\n",
    "gitp_quad2d = interpolate(g, BSpline(Quadratic(Line())), OnCell())\n",
    "\n",
    "display(plot(x=xs,y=ys,z=g,Geom.contour))\n",
    "display(plot(x=1:.1:5, y=1:.1:8, z=[gitp_quad2d[x,y] for x in 1:.1:5, y in 1:.1:8], Geom.contour))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simple, huh? =)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extrapolation\n",
    "\n",
    "The behavior outside the domain is not carefully specified by `interpolate`, and most likely you should avoid using such objects outside the original domain. If you do want to control behavior outside the domain, you will likely be interested in `extrapolate`:\n",
    "\n",
    "    yextr = extrapolate(yitp, Flat)\n",
    "\n",
    "The `Flat` parameter to `extrapolate` signifies that we want the value to remain constant (flat) outside of the domain. Other options are...(how the interpolation object will behave when an index outside of the domain $[1, 10]$ is given; with `ExtrapError`, a `BoundsError` is thrown, while with `ExtrapNaN` (the only other implemented extrapolation behavior at the moment), `NaN` is returned.)\n",
    "\n"
   ]
  }
 ],
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